In this paper,we propose enhancements to Beetle Antennae search(BAS)algorithm,called BAS-ADAIVL to smoothen the convergence behavior and avoid trapping in localminima for a highly noin-convex objective function.We ach...In this paper,we propose enhancements to Beetle Antennae search(BAS)algorithm,called BAS-ADAIVL to smoothen the convergence behavior and avoid trapping in localminima for a highly noin-convex objective function.We achieve this by adaptively adjusting the step-size in each iteration using the adaptive moment estimation(ADAM)update rule.The proposed algorithm also increases the convergence rate in a narrow valley.A key feature of the ADAM update rule is the ability to adjust the step-size for each dimension separately instead of using the same step-size.Since ADAM is traditionally used with gradient-based optimization algorithms,therefore we first propose a gradient estimation model without the need to differentiate the objective function.Resultantly,it demonstrates excellent performance and fast convergence rate in searching for the optimum of noin-convex functions.The efficiency of the proposed algorithm was tested on three different benchmark problems,including the training of a high-dimensional neural network.The performance is compared with particle swarm optimizer(PSO)and the original BAS algorithm.展开更多
Over the past years,many efforts have been accomplished to achieve fast and accurate meta-heuristic algorithms to optimize a variety of real-world problems.This study presents a new optimization method based on an unu...Over the past years,many efforts have been accomplished to achieve fast and accurate meta-heuristic algorithms to optimize a variety of real-world problems.This study presents a new optimization method based on an unusual geological phenomenon in nature,named Geyser inspired Algorithm(GEA).The mathematical modeling of this geological phenomenon is carried out to have a better understanding of the optimization process.The efficiency and accuracy of GEA are verified using statistical examination and convergence rate comparison on numerous CEC 2005,CEC 2014,CEC 2017,and real-parameter benchmark functions.Moreover,GEA has been applied to several real-parameter engineering optimization problems to evaluate its effectiveness.In addition,to demonstrate the applicability and robustness of GEA,a comprehensive investigation is performed for a fair comparison with other standard optimization methods.The results demonstrate that GEA is noticeably prosperous in reaching the optimal solutions with a high convergence rate in comparison with other well-known nature-inspired algorithms,including ABC,BBO,PSO,and RCGA.Note that the source code of the GEA is publicly available at https://www.optim-app.com/projects/gea.展开更多
Feature Selection(FS)is considered as an important preprocessing step in data mining and is used to remove redundant or unrelated features from high-dimensional data.Most optimization algorithms for FS problems are no...Feature Selection(FS)is considered as an important preprocessing step in data mining and is used to remove redundant or unrelated features from high-dimensional data.Most optimization algorithms for FS problems are not balanced in search.A hybrid algorithm called nonlinear binary grasshopper whale optimization algorithm(NL-BGWOA)is proposed to solve the problem in this paper.In the proposed method,a new position updating strategy combining the position changes of whales and grasshoppers population is expressed,which optimizes the diversity of searching in the target domain.Ten distinct high-dimensional UCI datasets,the multi-modal Parkinson's speech datasets,and the COVID-19 symptom dataset are used to validate the proposed method.It has been demonstrated that the proposed NL-BGWOA performs well across most of high-dimensional datasets,which shows a high accuracy rate of up to 0.9895.Furthermore,the experimental results on the medical datasets also demonstrate the advantages of the proposed method in actual FS problem,including accuracy,size of feature subsets,and fitness with best values of 0.913,5.7,and 0.0873,respectively.The results reveal that the proposed NL-BGWOA has comprehensive superiority in solving the FS problem of high-dimensional data.展开更多
This paper proposes a new stochastic optimizer called the Colony Predation Algorithm(CPA)based on the corporate predation of animals in nature.CPA utilizes a mathematical mapping following the strategies used by anima...This paper proposes a new stochastic optimizer called the Colony Predation Algorithm(CPA)based on the corporate predation of animals in nature.CPA utilizes a mathematical mapping following the strategies used by animal hunting groups,such as dispersing prey,encircling prey,supporting the most likely successful hunter,and seeking another target.Moreover,the proposed CPA introduces new features of a unique mathematical model that uses a success rate to adjust the strategy and simulate hunting animals'selective abandonment behavior.This paper also presents a new way to deal with cross-border situations,whereby the optimal position value of a cross-border situation replaces the cross-border value to improve the algorithm's exploitation ability.The proposed CPA was compared with state-of-the-art metaheuristics on a comprehensive set of benchmark functions for performance verification and on five classical engineering design problems to evaluate the algorithm's efficacy in optimizing engineering problems.The results show that the proposed algorithm exhibits competitive,superior performance in different search landscapes over the other algorithms.Moreover,the source code of the CPA will be publicly available after publication.展开更多
Despite the engineering potential by the co-existence of inorganic and organic substances to protect vulnerable metallic materials from corrosive environments,both their interaction and in-situ formation mechanism to ...Despite the engineering potential by the co-existence of inorganic and organic substances to protect vulnerable metallic materials from corrosive environments,both their interaction and in-situ formation mechanism to induce the nature-inspired composite remained less understood.The present work used three distinctive mercaptobenzazole(MB)compounds working as corrosion inhibitors,such as 2-mercaptobenzoxazole(MBO),2-mercaptobenzothiazole(MBT),and 2-mercaptobenzimidazole(MBI)in a bid to understand how the geometrical structure arising from O,S,and N atoms affected the interaction toward inorganic layer.MB compounds that were used here to control the corrosion kinetics would be interacted readily with the pre-existing MgO layer fabricated by plasma electrolysis.This phenomenon triggered the nucleation of the root network since MB compounds were seen to be adsorbed actively on the defective surface through the active sites in MB compound.Then,the molecule with twin donor atoms adjacent to the mercapto-sites affected the facile growth of the grass-like structures with‘uniform’distribution via molecular self-assembly,which showed better corrosion performance than those with having dissimilar donor atoms with the inhibition efficiency(η)of 97%approximately.The formation mechanism underlying nucleation and growth behavior of MB molecule was discussed concerning the theoretical calculation of density functional theory.展开更多
The utilization of recycled aggregates(RA)for concrete production has the potential to offer substantial environmental and economic advantages.However,RA concrete is plagued with considerable durability concerns,parti...The utilization of recycled aggregates(RA)for concrete production has the potential to offer substantial environmental and economic advantages.However,RA concrete is plagued with considerable durability concerns,particularly carbonation.To advance the application of RA concrete,the establishment of a reliable model for predicting the carbonation is needed.On the one hand,concrete carbonation is a long and slow process and thus consumes a lot of time and energy to monitor.On the other hand,carbonation is influenced by many factors and is hard to predict.Regarding this,this paper proposes the use of machine learning techniques to establish accurate prediction models for the carbonation depth(CD)of RA concrete.Three types of regression techniques and meta-heuristic algorithms were employed to provide more alternative predictive tools.It was found that the best prediction performance was obtained from extreme gradient boosting-multi-universe optimizer(XGB-MVO)with R^(2) value of 0.9949 and 0.9398 for training and testing sets,respectively.XGB-MVO was used for evaluating physical laws of carbonation and it was found that the developed XGB-MVO model could provide reasonable predictions when new data were investigated.It also showed better generalization capabilities when compared with different models in the literature.Overall,this paper emphasizes the need for sustainable solutions in the construction industry to reduce its environmental impact and contribute to sustainable and low-carbon economies.展开更多
Human civilization has been relentlessly inspired by the nurturing lessons;nature is teaching us.From birds to airplanes and bullet trains,nature gave us a lot of perspective in aiding the progress and development of ...Human civilization has been relentlessly inspired by the nurturing lessons;nature is teaching us.From birds to airplanes and bullet trains,nature gave us a lot of perspective in aiding the progress and development of countless industries,inventions,transportation,and many more.Not only that nature inspired us in such technological advances but also,nature stimulated the advancement of micro-and nanostructures.Nature-inspired nanoarchitectures have been consid-ered a favorable structure in electrode materials for a wide range of applications.It offers various positive attributes,especially in energy storage applications,such as the formation of hierarchical two-dimen-sional and three-dimensional interconnected networked structures that benefit the electrodes in terms of high surface area,high porosity and rich surface textural features,and eventually,delivering high capacity and outstanding overall material stability.In this review,we compre-hensively assessed and compiled the recent advances in various nature-inspired based on animal-and human-inspired nanostructures used for supercapacitors.This comprehensive review will help researchers to accommodate nature-inspired nanostructures in industrializing energy storage and many other applications.展开更多
Farmland Fertility Algorithm(FFA)is a recent nature-inspired metaheuristic algorithm for solving optimization problems.Nevertheless,FFA has some drawbacks:slow convergence and imbalance of diversification(exploration)...Farmland Fertility Algorithm(FFA)is a recent nature-inspired metaheuristic algorithm for solving optimization problems.Nevertheless,FFA has some drawbacks:slow convergence and imbalance of diversification(exploration)and intensification(exploitation).An adaptive mechanism in every algorithm can achieve a proper balance between exploration and exploitation.The literature shows that chaotic maps are incorporated into metaheuristic algorithms to eliminate these drawbacks.Therefore,in this paper,twelve chaotic maps have been embedded into FFA to find the best numbers of prospectors to increase the exploitation of the best promising solutions.Furthermore,the Quasi-Oppositional-Based Learning(QOBL)mechanism enhances the exploration speed and convergence rate;we name a CQFFA algorithm.The improvements have been made in line with the weaknesses of the FFA algorithm because the FFA algorithm has fallen into the optimal local trap in solving some complex problems or does not have sufficient ability in the intensification component.The results obtained show that the proposed CQFFA model has been significantly improved.It is applied to twenty-three widely-used test functions and compared with similar state-of-the-art algorithms statistically and visually.Also,the CQFFA algorithm has evaluated six real-world engineering problems.The experimental results showed that the CQFFA algorithm outperforms other competitor algorithms.展开更多
This paper proposes a modified version of the Dwarf Mongoose Optimization Algorithm (IDMO) for constrained engineering design problems. This optimization technique modifies the base algorithm (DMO) in three simple but...This paper proposes a modified version of the Dwarf Mongoose Optimization Algorithm (IDMO) for constrained engineering design problems. This optimization technique modifies the base algorithm (DMO) in three simple but effective ways. First, the alpha selection in IDMO differs from the DMO, where evaluating the probability value of each fitness is just a computational overhead and contributes nothing to the quality of the alpha or other group members. The fittest dwarf mongoose is selected as the alpha, and a new operator ω is introduced, which controls the alpha movement, thereby enhancing the exploration ability and exploitability of the IDMO. Second, the scout group movements are modified by randomization to introduce diversity in the search process and explore unvisited areas. Finally, the babysitter's exchange criterium is modified such that once the criterium is met, the babysitters that are exchanged interact with the dwarf mongoose exchanging them to gain information about food sources and sleeping mounds, which could result in better-fitted mongooses instead of initializing them afresh as done in DMO, then the counter is reset to zero. The proposed IDMO was used to solve the classical and CEC 2020 benchmark functions and 12 continuous/discrete engineering optimization problems. The performance of the IDMO, using different performance metrics and statistical analysis, is compared with the DMO and eight other existing algorithms. In most cases, the results show that solutions achieved by the IDMO are better than those obtained by the existing algorithms.展开更多
Atherosclerosis(AS)has emerged as one of the prevalent arterial vascular diseases characterized by plaque and inflammation,primarily causing disability and mortality globally.Drug therapy remains the main treatment fo...Atherosclerosis(AS)has emerged as one of the prevalent arterial vascular diseases characterized by plaque and inflammation,primarily causing disability and mortality globally.Drug therapy remains the main treatment for AS.However,a series of obstacles hinder effective drug delivery.Nature,from natural micro-/nano-structural biological particles like natural cells and extracellular vesicles to the distinctions between the normal and pathological microenvironment,offers compelling solutions for efficient drug delivery.Nature-inspired nanocarriers of synthetic stimulus-responsive materials and natural components,such as lipids,proteins and membrane structures,have emerged as promising candidates for fulfilling drug delivery needs.These nanocarriers offer several advantages,including prolonged blood circulation,targeted plaque delivery,targeted specific cells delivery and controlled drug release at the action site.In this review,we discuss the nature-inspired nanocarriers which leverage the natural properties of cells or the microenvironment to improve atherosclerotic drug therapy.Finally,we provide an overview of the challenges and opportunities of applying these innovative nature-inspired nanocarriers.展开更多
Software needs modifications and requires revisions regularly.Owing to these revisions,retesting software becomes essential to ensure that the enhancements made,have not affected its bug-free functioning.The time and ...Software needs modifications and requires revisions regularly.Owing to these revisions,retesting software becomes essential to ensure that the enhancements made,have not affected its bug-free functioning.The time and cost incurred in this process,need to be reduced by the method of test case selection and prioritization.It is observed that many nature-inspired techniques are applied in this area.African Buffalo Optimization is one such approach,applied to regression test selection and prioritization.In this paper,the proposed work explains and proves the applicability of the African Buffalo Optimization approach to test case selection and prioritization.The proposed algorithm converges in polynomial time(O(n^(2))).In this paper,the empirical evaluation of applying African Buffalo Optimization for test case prioritization is done on sample data set with multiple iterations.An astounding 62.5%drop in size and a 48.57%drop in the runtime of the original test suite were recorded.The obtained results are compared with Ant Colony Optimization.The comparative analysis indicates that African Buffalo Optimization and Ant Colony Optimization exhibit similar fault detection capabilities(80%),and a reduction in the overall execution time and size of the resultant test suite.The results and analysis,hence,advocate and encourages the use of African Buffalo Optimization in the area of test case selection and prioritization.展开更多
Chimp Optimization Algorithm(ChOA)is one of the recent metaheuristics swarm intelligence methods.It has been widely tailored for a wide variety of optimization problems due to its impressive characteristics over other...Chimp Optimization Algorithm(ChOA)is one of the recent metaheuristics swarm intelligence methods.It has been widely tailored for a wide variety of optimization problems due to its impressive characteristics over other swarm intelligence methods:it has very few parameters,and no derivation information is required in the initial search.Also,it is simple,easy to use,flexible,scalable,and has a special capability to strike the right balance between exploration and exploitation during the search which leads to favorable convergence.Therefore,the ChOA has recently gained a very big research interest with tremendous audiences from several domains in a very short time.Thus,in this review paper,several research publications using ChOA have been overviewed and summarized.Initially,introductory information about ChOA is provided which illustrates the natural foundation context and its related optimization conceptual framework.The main operations of ChOA are procedurally discussed,and the theoretical foundation is described.Furthermore,the recent versions of ChOA are discussed in detail which are categorized into modified,hybridized,and paralleled versions.The main applications of ChOA are also thoroughly described.The applications belong to the domains of economics,image processing,engineering,neural network,power and energy,networks,etc.Evaluation of ChOA is also provided.The review paper will be helpful for the researchers and practitioners of ChOA belonging to a wide range of audiences from the domains of optimization,engineering,medical,data mining,and clustering.As well,it is wealthy in research on health,environment,and public safety.Also,it will aid those who are interested by providing them with potential future research.展开更多
In the context of both the Virtual Power Plant (VPP) and microgrid(MG), the Energy Management System (EMS) is a key decision-maker forintegrating Distributed renewable Energy Resources (DERs) efficiently. TheEMS is re...In the context of both the Virtual Power Plant (VPP) and microgrid(MG), the Energy Management System (EMS) is a key decision-maker forintegrating Distributed renewable Energy Resources (DERs) efficiently. TheEMS is regarded as a strong enabler of providing the optimized schedulingcontrol in operation and management of usage of disperse DERs and RenewableEnergy reSources (RES) such as a small-size wind-turbine (WT) andphotovoltaic (PV) energies. The main objective to be pursued by the EMSis the minimization of the overall operating cost of the MG integrated VPPnetwork. However, the minimization of the power peaks is a new objective andopen issue to a well-functional EMS, along with the maximization of profitin the energy market. Thus, both objectives have to be taken into accountat the same time. Thus, this paper proposes the EMS application incorporatingpower offering strategy applying a nature-inspired algorithm such asParticle Swarm Optimization (PSO) algorithm, in order to find the optimalsolution of the objective function in the context of the overall operating cost,the coordination of DERs, and the energy losses in a MG integrated VPPnetwork. For a fair DERs coordination with minimized power fluctuationsin the power flow, the power offering strategies with an active power controland re-distribution are proposed. Simulation results show that the proposedMG integrated VPP model with PSO-based EMS employing EgalitarianreDistribution (ED) power offering strategy is most feasible option for theoverall operating cost of VPP revenue. The total operating cost of the proposedEMS with ED strategy is 40.98$ compared to 432.8$ of MGs only withoutEMS. It is concluded that each MGs in the proposed VPP model intelligentlyparticipates in energy trading market compliant with the objective function,to minimize the overall cost and the power fluctuation.展开更多
In this paper, a recently developed nature-inspired optimization algorithm called the hydrological cycle algorithm (HCA) is evaluated on the traveling salesman problem (TSP). The HCA is based on the continuous movemen...In this paper, a recently developed nature-inspired optimization algorithm called the hydrological cycle algorithm (HCA) is evaluated on the traveling salesman problem (TSP). The HCA is based on the continuous movement of water drops in the natural hydrological cycle. The HCA performance is tested on various geometric structures and standard benchmarks instances. The HCA has successfully solved TSPs and obtained the optimal solution for 20 of 24 benchmarked instances, and near-optimal for the rest. The obtained results illustrate the efficiency of using HCA for solving discrete domain optimization problems. The solution quality and number of iterations were compared with those of other metaheuristic algorithms. The comparisons demonstrate the effectiveness of the HCA.展开更多
Bioadhesives act as a bridge in wound closure by forming an effective interface to protect against liquid and gas leakage and aid the stoppage of bleeding.To their credit,tissue adhesives have made an indelible impact...Bioadhesives act as a bridge in wound closure by forming an effective interface to protect against liquid and gas leakage and aid the stoppage of bleeding.To their credit,tissue adhesives have made an indelible impact on almost all wound-related surgeries.Their unique properties include minimal damage to tissues,low chance of infection,ease of use and short wound-closure time.In contrast,classic closures,like suturing and stapling,exhibit potential additional complications with long operation times and undesirable inflammatory responses.Although tremendous progress has been made in the development of tissue adhesives,they are not yet ideal.Therefore,highlighting and summarizing existing adhesive designs and synthesis,and comparing the different products will contribute to future development.This review first provides a summary of current commercial traditional tissue adhesives.Then,based on adhesion interaction mechanisms,the tissue adhesives are categorized into three main types:adhesive patches that bind molecularly with tissue,tissuestitching adhesives based on pre-polymer or precursor solutions,and bioinspired or biomimetic tissue adhesives.Their specific adhesion mechanisms,properties and related applications are discussed.The adhesion mechanisms of commercial traditional adhesives as well as their limitations and shortcomings are also reviewed.Finally,we also discuss the future perspectives of tissue adhesives.展开更多
To the editor:The precise diagnosis and effective therapy of refractory diseases,such as neurodegenerative diseases,coronary heart diseases and cancers,is critical to increase the survival rate and improve the life qu...To the editor:The precise diagnosis and effective therapy of refractory diseases,such as neurodegenerative diseases,coronary heart diseases and cancers,is critical to increase the survival rate and improve the life quality of patients.Most of the conventional small molecular medicines are heavily associated with the limitations of poor solubility,low targeting efficiency,inducing drug resistance and systemic toxicity,promoting scientists to develop new strategies for overcoming these limitations.展开更多
Concrete compressive strength prediction is an essential process for material design and sustainability.This study investigates several novel hybrid adaptive neuro-fuzzy inference system(ANFIS)evolutionary models,i.e....Concrete compressive strength prediction is an essential process for material design and sustainability.This study investigates several novel hybrid adaptive neuro-fuzzy inference system(ANFIS)evolutionary models,i.e.,ANFIS-particle swarm optimization(PSO),ANFIS-ant colony,ANFIS-differential evolution(DE),and ANFIS-genetic algorithm to predict the foamed concrete compressive strength.Several concrete properties,including cement content(C),oven dry density(O),water-to-binder ratio(W),and foamed volume(F)are used as input variables.A relevant data set is obtained from open-access published experimental investigations and used to build predictive models.The performance of the proposed predictive models is evaluated based on the mean performance(MP),which is the mean value of several statistical error indices.To optimize each predictive model and its input variables,univariate(C,O,W,and F),bivariate(C-O,C-W,C-F,O-W,O-F,and W-F),trivariate(C-O-W,C-W-F,O-W-F),and four-variate(C-O-W-F)combinations of input variables are constructed for each model.The results indicate that the best predictions obtained using the univariate,bivariate,trivariate,and four-variate models are ANFIS-DE-(O)(MP=0.96),ANFIS-PSO-(C-O)(MP=0.88),ANFIS-DE-(O-W-F)(MP=0.94),and ANFIS-PSO-(C-O-W-F)(MP=0.89),respectively.ANFIS-PSO-(C-O)yielded the best accurate prediction of compressive strength with an MP value of 0.96.展开更多
Design and fabrication of functional porous air cathode materials with superior catalytic activity is still the key point for non-aqueous lithium-oxygen(Li-O2) batteries. Herein, inspired by the self-standing three-di...Design and fabrication of functional porous air cathode materials with superior catalytic activity is still the key point for non-aqueous lithium-oxygen(Li-O2) batteries. Herein, inspired by the self-standing three-dimensional(3D) structure of the natural spinach leaves, a unique binder-free and self-standing porous Au/spinach cathode for high-performance Li-O2 batteries has been developed. The carbonized spinach leaves serve as a superconductive current collector and an ideal porous host for accommodating catalysts. The Au/spinach cathode could offer enough spaces for accommodating the discharge products, shorten the distance of the oxygen and electrolyte diffusion, and promote the oxygen reduction reaction(ORR) and oxygen evolution reaction (OER) processes. This optimized Au/spinach cathode achieved a high specific area capacity of 7.23 mA‧h/cm2 at a current density of 0.05 mA/cm2 and exhibited excellent stability(280 cycles at 0.05 mA/cm2 with a fixed capacity of 0.2 mA‧h/cm2). The superior performance encourages the construction of more advanced cathode architectures by the use of bio-composites for Li-O2 batteries.展开更多
We report a nature-inspired design of tree-like architecture of cuprous oxide(Cu2O)stem and cobalt oxides(CoO)branch serving as an efficient anode for sodium-ion batteries.The construction of stembranch architectures ...We report a nature-inspired design of tree-like architecture of cuprous oxide(Cu2O)stem and cobalt oxides(CoO)branch serving as an efficient anode for sodium-ion batteries.The construction of stembranch architectures involves the growth of Cu2O nanorods and the subsequent deposition of CoO nanowires.Due to abundant active sites and full exposure to electrolyte,such a Cu2O@CoO stem-branch architecture demonstrates a robust storage toward Na^+ions,affording a capacity retention of^100%over 300 continuous cycles and a remarkable rate capability of 296 mAh g^-1 at 1 A g^-1.This result clearly shows the potential of nature-inspired materials engineering may find extensive applications in the design of high-performance electrodes for rechargeable batteries.展开更多
In this paper we provide a novel approach for breaking a significant class of block ciphers, the so-called SPN ciphers, using the process of gene assembly in ciliates. Our proposed scheme utilizes, for the first time,...In this paper we provide a novel approach for breaking a significant class of block ciphers, the so-called SPN ciphers, using the process of gene assembly in ciliates. Our proposed scheme utilizes, for the first time, the Turing-powerful potential of gene assembly procedure of ciliated protozoa into the real world computations and has a fewer number of steps than the other proposed schemes to break a cipher. We elaborate notions of formal language theory based on AIR systems, which can be thought of as a modified version of intramolecular scheme to model the ciliate bio-operations, for construction of building blocks necessary for breaking the cipher, and based on these nature-inspired constructions which are as powerful as Turing machines, we propose a theoretical approach for breaking SPN ciphers. Then, we simulate our proposed plan for breaking these ciphers on a sample block cipher based on this structure. Our results show that the proposed scheme has 51.5 percent improvement over the best previously proposed nature-inspired scheme for breaking a cipher.展开更多
文摘In this paper,we propose enhancements to Beetle Antennae search(BAS)algorithm,called BAS-ADAIVL to smoothen the convergence behavior and avoid trapping in localminima for a highly noin-convex objective function.We achieve this by adaptively adjusting the step-size in each iteration using the adaptive moment estimation(ADAM)update rule.The proposed algorithm also increases the convergence rate in a narrow valley.A key feature of the ADAM update rule is the ability to adjust the step-size for each dimension separately instead of using the same step-size.Since ADAM is traditionally used with gradient-based optimization algorithms,therefore we first propose a gradient estimation model without the need to differentiate the objective function.Resultantly,it demonstrates excellent performance and fast convergence rate in searching for the optimum of noin-convex functions.The efficiency of the proposed algorithm was tested on three different benchmark problems,including the training of a high-dimensional neural network.The performance is compared with particle swarm optimizer(PSO)and the original BAS algorithm.
文摘Over the past years,many efforts have been accomplished to achieve fast and accurate meta-heuristic algorithms to optimize a variety of real-world problems.This study presents a new optimization method based on an unusual geological phenomenon in nature,named Geyser inspired Algorithm(GEA).The mathematical modeling of this geological phenomenon is carried out to have a better understanding of the optimization process.The efficiency and accuracy of GEA are verified using statistical examination and convergence rate comparison on numerous CEC 2005,CEC 2014,CEC 2017,and real-parameter benchmark functions.Moreover,GEA has been applied to several real-parameter engineering optimization problems to evaluate its effectiveness.In addition,to demonstrate the applicability and robustness of GEA,a comprehensive investigation is performed for a fair comparison with other standard optimization methods.The results demonstrate that GEA is noticeably prosperous in reaching the optimal solutions with a high convergence rate in comparison with other well-known nature-inspired algorithms,including ABC,BBO,PSO,and RCGA.Note that the source code of the GEA is publicly available at https://www.optim-app.com/projects/gea.
基金supported by Natural Science Foundation of Liaoning Province under Grant 2021-MS-272Educational Committee project of Liaoning Province under Grant LJKQZ2021088.
文摘Feature Selection(FS)is considered as an important preprocessing step in data mining and is used to remove redundant or unrelated features from high-dimensional data.Most optimization algorithms for FS problems are not balanced in search.A hybrid algorithm called nonlinear binary grasshopper whale optimization algorithm(NL-BGWOA)is proposed to solve the problem in this paper.In the proposed method,a new position updating strategy combining the position changes of whales and grasshoppers population is expressed,which optimizes the diversity of searching in the target domain.Ten distinct high-dimensional UCI datasets,the multi-modal Parkinson's speech datasets,and the COVID-19 symptom dataset are used to validate the proposed method.It has been demonstrated that the proposed NL-BGWOA performs well across most of high-dimensional datasets,which shows a high accuracy rate of up to 0.9895.Furthermore,the experimental results on the medical datasets also demonstrate the advantages of the proposed method in actual FS problem,including accuracy,size of feature subsets,and fitness with best values of 0.913,5.7,and 0.0873,respectively.The results reveal that the proposed NL-BGWOA has comprehensive superiority in solving the FS problem of high-dimensional data.
基金This research was supported by the National Natural Science Foundation of China(62076185,U1809209).
文摘This paper proposes a new stochastic optimizer called the Colony Predation Algorithm(CPA)based on the corporate predation of animals in nature.CPA utilizes a mathematical mapping following the strategies used by animal hunting groups,such as dispersing prey,encircling prey,supporting the most likely successful hunter,and seeking another target.Moreover,the proposed CPA introduces new features of a unique mathematical model that uses a success rate to adjust the strategy and simulate hunting animals'selective abandonment behavior.This paper also presents a new way to deal with cross-border situations,whereby the optimal position value of a cross-border situation replaces the cross-border value to improve the algorithm's exploitation ability.The proposed CPA was compared with state-of-the-art metaheuristics on a comprehensive set of benchmark functions for performance verification and on five classical engineering design problems to evaluate the algorithm's efficacy in optimizing engineering problems.The results show that the proposed algorithm exhibits competitive,superior performance in different search landscapes over the other algorithms.Moreover,the source code of the CPA will be publicly available after publication.
基金supported by the Fundamental-Core National Project of the National Research Foundation(NRF)funded by the Ministry of Science and ICT,Republic of Korea with the grant number 2022R1F1A1072739.
文摘Despite the engineering potential by the co-existence of inorganic and organic substances to protect vulnerable metallic materials from corrosive environments,both their interaction and in-situ formation mechanism to induce the nature-inspired composite remained less understood.The present work used three distinctive mercaptobenzazole(MB)compounds working as corrosion inhibitors,such as 2-mercaptobenzoxazole(MBO),2-mercaptobenzothiazole(MBT),and 2-mercaptobenzimidazole(MBI)in a bid to understand how the geometrical structure arising from O,S,and N atoms affected the interaction toward inorganic layer.MB compounds that were used here to control the corrosion kinetics would be interacted readily with the pre-existing MgO layer fabricated by plasma electrolysis.This phenomenon triggered the nucleation of the root network since MB compounds were seen to be adsorbed actively on the defective surface through the active sites in MB compound.Then,the molecule with twin donor atoms adjacent to the mercapto-sites affected the facile growth of the grass-like structures with‘uniform’distribution via molecular self-assembly,which showed better corrosion performance than those with having dissimilar donor atoms with the inhibition efficiency(η)of 97%approximately.The formation mechanism underlying nucleation and growth behavior of MB molecule was discussed concerning the theoretical calculation of density functional theory.
基金the funding supported by China Scholarship Council(Nos.202008440524 and 202006370006)partially supported by the Distinguished Youth Science Foundation of Hunan Province of China(No.2022JJ10073)+1 种基金the Innovation Driven Project of Central South University(No.2020CX040)Shenzhen Science and Technology Plan(No.JCYJ20190808123013260).
文摘The utilization of recycled aggregates(RA)for concrete production has the potential to offer substantial environmental and economic advantages.However,RA concrete is plagued with considerable durability concerns,particularly carbonation.To advance the application of RA concrete,the establishment of a reliable model for predicting the carbonation is needed.On the one hand,concrete carbonation is a long and slow process and thus consumes a lot of time and energy to monitor.On the other hand,carbonation is influenced by many factors and is hard to predict.Regarding this,this paper proposes the use of machine learning techniques to establish accurate prediction models for the carbonation depth(CD)of RA concrete.Three types of regression techniques and meta-heuristic algorithms were employed to provide more alternative predictive tools.It was found that the best prediction performance was obtained from extreme gradient boosting-multi-universe optimizer(XGB-MVO)with R^(2) value of 0.9949 and 0.9398 for training and testing sets,respectively.XGB-MVO was used for evaluating physical laws of carbonation and it was found that the developed XGB-MVO model could provide reasonable predictions when new data were investigated.It also showed better generalization capabilities when compared with different models in the literature.Overall,this paper emphasizes the need for sustainable solutions in the construction industry to reduce its environmental impact and contribute to sustainable and low-carbon economies.
基金This work was supported by the Donations for Research Projects_RMGS(project number 9229006)M.A.A.expresses appreciation to the Deanship of Scientific Research at King Khalid University Saudi Arabia(R.G.P.2/40/43).
文摘Human civilization has been relentlessly inspired by the nurturing lessons;nature is teaching us.From birds to airplanes and bullet trains,nature gave us a lot of perspective in aiding the progress and development of countless industries,inventions,transportation,and many more.Not only that nature inspired us in such technological advances but also,nature stimulated the advancement of micro-and nanostructures.Nature-inspired nanoarchitectures have been consid-ered a favorable structure in electrode materials for a wide range of applications.It offers various positive attributes,especially in energy storage applications,such as the formation of hierarchical two-dimen-sional and three-dimensional interconnected networked structures that benefit the electrodes in terms of high surface area,high porosity and rich surface textural features,and eventually,delivering high capacity and outstanding overall material stability.In this review,we compre-hensively assessed and compiled the recent advances in various nature-inspired based on animal-and human-inspired nanostructures used for supercapacitors.This comprehensive review will help researchers to accommodate nature-inspired nanostructures in industrializing energy storage and many other applications.
文摘Farmland Fertility Algorithm(FFA)is a recent nature-inspired metaheuristic algorithm for solving optimization problems.Nevertheless,FFA has some drawbacks:slow convergence and imbalance of diversification(exploration)and intensification(exploitation).An adaptive mechanism in every algorithm can achieve a proper balance between exploration and exploitation.The literature shows that chaotic maps are incorporated into metaheuristic algorithms to eliminate these drawbacks.Therefore,in this paper,twelve chaotic maps have been embedded into FFA to find the best numbers of prospectors to increase the exploitation of the best promising solutions.Furthermore,the Quasi-Oppositional-Based Learning(QOBL)mechanism enhances the exploration speed and convergence rate;we name a CQFFA algorithm.The improvements have been made in line with the weaknesses of the FFA algorithm because the FFA algorithm has fallen into the optimal local trap in solving some complex problems or does not have sufficient ability in the intensification component.The results obtained show that the proposed CQFFA model has been significantly improved.It is applied to twenty-three widely-used test functions and compared with similar state-of-the-art algorithms statistically and visually.Also,the CQFFA algorithm has evaluated six real-world engineering problems.The experimental results showed that the CQFFA algorithm outperforms other competitor algorithms.
文摘This paper proposes a modified version of the Dwarf Mongoose Optimization Algorithm (IDMO) for constrained engineering design problems. This optimization technique modifies the base algorithm (DMO) in three simple but effective ways. First, the alpha selection in IDMO differs from the DMO, where evaluating the probability value of each fitness is just a computational overhead and contributes nothing to the quality of the alpha or other group members. The fittest dwarf mongoose is selected as the alpha, and a new operator ω is introduced, which controls the alpha movement, thereby enhancing the exploration ability and exploitability of the IDMO. Second, the scout group movements are modified by randomization to introduce diversity in the search process and explore unvisited areas. Finally, the babysitter's exchange criterium is modified such that once the criterium is met, the babysitters that are exchanged interact with the dwarf mongoose exchanging them to gain information about food sources and sleeping mounds, which could result in better-fitted mongooses instead of initializing them afresh as done in DMO, then the counter is reset to zero. The proposed IDMO was used to solve the classical and CEC 2020 benchmark functions and 12 continuous/discrete engineering optimization problems. The performance of the IDMO, using different performance metrics and statistical analysis, is compared with the DMO and eight other existing algorithms. In most cases, the results show that solutions achieved by the IDMO are better than those obtained by the existing algorithms.
基金supported by the National Natural Science Foundation of China(NSFC 32071323 and 32271410)Program for Innovative Research Team in Science and Technology in Fujian Province University,Scientific Research Funds of Huaqiao University(22BS125).
文摘Atherosclerosis(AS)has emerged as one of the prevalent arterial vascular diseases characterized by plaque and inflammation,primarily causing disability and mortality globally.Drug therapy remains the main treatment for AS.However,a series of obstacles hinder effective drug delivery.Nature,from natural micro-/nano-structural biological particles like natural cells and extracellular vesicles to the distinctions between the normal and pathological microenvironment,offers compelling solutions for efficient drug delivery.Nature-inspired nanocarriers of synthetic stimulus-responsive materials and natural components,such as lipids,proteins and membrane structures,have emerged as promising candidates for fulfilling drug delivery needs.These nanocarriers offer several advantages,including prolonged blood circulation,targeted plaque delivery,targeted specific cells delivery and controlled drug release at the action site.In this review,we discuss the nature-inspired nanocarriers which leverage the natural properties of cells or the microenvironment to improve atherosclerotic drug therapy.Finally,we provide an overview of the challenges and opportunities of applying these innovative nature-inspired nanocarriers.
基金This research is funded by the Deanship of Scientific Research at Umm Al-Qura University,Grant Code:22UQU4281755DSR02.
文摘Software needs modifications and requires revisions regularly.Owing to these revisions,retesting software becomes essential to ensure that the enhancements made,have not affected its bug-free functioning.The time and cost incurred in this process,need to be reduced by the method of test case selection and prioritization.It is observed that many nature-inspired techniques are applied in this area.African Buffalo Optimization is one such approach,applied to regression test selection and prioritization.In this paper,the proposed work explains and proves the applicability of the African Buffalo Optimization approach to test case selection and prioritization.The proposed algorithm converges in polynomial time(O(n^(2))).In this paper,the empirical evaluation of applying African Buffalo Optimization for test case prioritization is done on sample data set with multiple iterations.An astounding 62.5%drop in size and a 48.57%drop in the runtime of the original test suite were recorded.The obtained results are compared with Ant Colony Optimization.The comparative analysis indicates that African Buffalo Optimization and Ant Colony Optimization exhibit similar fault detection capabilities(80%),and a reduction in the overall execution time and size of the resultant test suite.The results and analysis,hence,advocate and encourages the use of African Buffalo Optimization in the area of test case selection and prioritization.
基金The authors would like to thank the Deanship of Scientific Research at Umm Al-Qura University for supporting this work by Grant Code:(23UQU4361183DSR03).
文摘Chimp Optimization Algorithm(ChOA)is one of the recent metaheuristics swarm intelligence methods.It has been widely tailored for a wide variety of optimization problems due to its impressive characteristics over other swarm intelligence methods:it has very few parameters,and no derivation information is required in the initial search.Also,it is simple,easy to use,flexible,scalable,and has a special capability to strike the right balance between exploration and exploitation during the search which leads to favorable convergence.Therefore,the ChOA has recently gained a very big research interest with tremendous audiences from several domains in a very short time.Thus,in this review paper,several research publications using ChOA have been overviewed and summarized.Initially,introductory information about ChOA is provided which illustrates the natural foundation context and its related optimization conceptual framework.The main operations of ChOA are procedurally discussed,and the theoretical foundation is described.Furthermore,the recent versions of ChOA are discussed in detail which are categorized into modified,hybridized,and paralleled versions.The main applications of ChOA are also thoroughly described.The applications belong to the domains of economics,image processing,engineering,neural network,power and energy,networks,etc.Evaluation of ChOA is also provided.The review paper will be helpful for the researchers and practitioners of ChOA belonging to a wide range of audiences from the domains of optimization,engineering,medical,data mining,and clustering.As well,it is wealthy in research on health,environment,and public safety.Also,it will aid those who are interested by providing them with potential future research.
文摘In the context of both the Virtual Power Plant (VPP) and microgrid(MG), the Energy Management System (EMS) is a key decision-maker forintegrating Distributed renewable Energy Resources (DERs) efficiently. TheEMS is regarded as a strong enabler of providing the optimized schedulingcontrol in operation and management of usage of disperse DERs and RenewableEnergy reSources (RES) such as a small-size wind-turbine (WT) andphotovoltaic (PV) energies. The main objective to be pursued by the EMSis the minimization of the overall operating cost of the MG integrated VPPnetwork. However, the minimization of the power peaks is a new objective andopen issue to a well-functional EMS, along with the maximization of profitin the energy market. Thus, both objectives have to be taken into accountat the same time. Thus, this paper proposes the EMS application incorporatingpower offering strategy applying a nature-inspired algorithm such asParticle Swarm Optimization (PSO) algorithm, in order to find the optimalsolution of the objective function in the context of the overall operating cost,the coordination of DERs, and the energy losses in a MG integrated VPPnetwork. For a fair DERs coordination with minimized power fluctuationsin the power flow, the power offering strategies with an active power controland re-distribution are proposed. Simulation results show that the proposedMG integrated VPP model with PSO-based EMS employing EgalitarianreDistribution (ED) power offering strategy is most feasible option for theoverall operating cost of VPP revenue. The total operating cost of the proposedEMS with ED strategy is 40.98$ compared to 432.8$ of MGs only withoutEMS. It is concluded that each MGs in the proposed VPP model intelligentlyparticipates in energy trading market compliant with the objective function,to minimize the overall cost and the power fluctuation.
文摘In this paper, a recently developed nature-inspired optimization algorithm called the hydrological cycle algorithm (HCA) is evaluated on the traveling salesman problem (TSP). The HCA is based on the continuous movement of water drops in the natural hydrological cycle. The HCA performance is tested on various geometric structures and standard benchmarks instances. The HCA has successfully solved TSPs and obtained the optimal solution for 20 of 24 benchmarked instances, and near-optimal for the rest. The obtained results illustrate the efficiency of using HCA for solving discrete domain optimization problems. The solution quality and number of iterations were compared with those of other metaheuristic algorithms. The comparisons demonstrate the effectiveness of the HCA.
文摘Bioadhesives act as a bridge in wound closure by forming an effective interface to protect against liquid and gas leakage and aid the stoppage of bleeding.To their credit,tissue adhesives have made an indelible impact on almost all wound-related surgeries.Their unique properties include minimal damage to tissues,low chance of infection,ease of use and short wound-closure time.In contrast,classic closures,like suturing and stapling,exhibit potential additional complications with long operation times and undesirable inflammatory responses.Although tremendous progress has been made in the development of tissue adhesives,they are not yet ideal.Therefore,highlighting and summarizing existing adhesive designs and synthesis,and comparing the different products will contribute to future development.This review first provides a summary of current commercial traditional tissue adhesives.Then,based on adhesion interaction mechanisms,the tissue adhesives are categorized into three main types:adhesive patches that bind molecularly with tissue,tissuestitching adhesives based on pre-polymer or precursor solutions,and bioinspired or biomimetic tissue adhesives.Their specific adhesion mechanisms,properties and related applications are discussed.The adhesion mechanisms of commercial traditional adhesives as well as their limitations and shortcomings are also reviewed.Finally,we also discuss the future perspectives of tissue adhesives.
基金supported by the National Natural Science Foundation of China(32071374 and 81761148029)the Program of Shanghai Academic Research Leader under the Science and Technology Innovation Action Plan(21XD1422100,China)+2 种基金Leading Talent of“Ten Thousand Plan”-National High-Level Talents Special Support Plan,the One Belt and One Road International Cooperation Project from the Key Research and Development Program of Zhejiang Province(2019C04024,China)the Zhejiang Provincial Natural Science Foundation of China(LR22C100001,China)the innovative research team of highlevel local universities in Shanghai(SHSMU-ZDCX20210900,China).
文摘To the editor:The precise diagnosis and effective therapy of refractory diseases,such as neurodegenerative diseases,coronary heart diseases and cancers,is critical to increase the survival rate and improve the life quality of patients.Most of the conventional small molecular medicines are heavily associated with the limitations of poor solubility,low targeting efficiency,inducing drug resistance and systemic toxicity,promoting scientists to develop new strategies for overcoming these limitations.
文摘Concrete compressive strength prediction is an essential process for material design and sustainability.This study investigates several novel hybrid adaptive neuro-fuzzy inference system(ANFIS)evolutionary models,i.e.,ANFIS-particle swarm optimization(PSO),ANFIS-ant colony,ANFIS-differential evolution(DE),and ANFIS-genetic algorithm to predict the foamed concrete compressive strength.Several concrete properties,including cement content(C),oven dry density(O),water-to-binder ratio(W),and foamed volume(F)are used as input variables.A relevant data set is obtained from open-access published experimental investigations and used to build predictive models.The performance of the proposed predictive models is evaluated based on the mean performance(MP),which is the mean value of several statistical error indices.To optimize each predictive model and its input variables,univariate(C,O,W,and F),bivariate(C-O,C-W,C-F,O-W,O-F,and W-F),trivariate(C-O-W,C-W-F,O-W-F),and four-variate(C-O-W-F)combinations of input variables are constructed for each model.The results indicate that the best predictions obtained using the univariate,bivariate,trivariate,and four-variate models are ANFIS-DE-(O)(MP=0.96),ANFIS-PSO-(C-O)(MP=0.88),ANFIS-DE-(O-W-F)(MP=0.94),and ANFIS-PSO-(C-O-W-F)(MP=0.89),respectively.ANFIS-PSO-(C-O)yielded the best accurate prediction of compressive strength with an MP value of 0.96.
基金This work was supported by the National Natural Science Foundation of China(Nos.51771177,51972141,21835002,21621001)the"111"Project of China(No.B17020)+6 种基金the Project of the Education Department of Jilin Province,China(No.JJKH20190113KJ)the Jilin Province Science and Technology Development Program,China(No.20190303104SF)the Jilin Province/Jilin University Co-construction Project-Funds for New Materials,China(No.SXGJSF2017-3)the Science and Technology Breakthrough Plan of Henan Province,China(Nos.202102210242,212102210186)the Key Scientific Research Project of Higher Education of Henan Province,China(No.21A150055)the Post Doctoral Innovative Talent Support Program,China(No.BX20180122)the China Postdoctoral Science Foundation,China(No.2019M651195).
文摘Design and fabrication of functional porous air cathode materials with superior catalytic activity is still the key point for non-aqueous lithium-oxygen(Li-O2) batteries. Herein, inspired by the self-standing three-dimensional(3D) structure of the natural spinach leaves, a unique binder-free and self-standing porous Au/spinach cathode for high-performance Li-O2 batteries has been developed. The carbonized spinach leaves serve as a superconductive current collector and an ideal porous host for accommodating catalysts. The Au/spinach cathode could offer enough spaces for accommodating the discharge products, shorten the distance of the oxygen and electrolyte diffusion, and promote the oxygen reduction reaction(ORR) and oxygen evolution reaction (OER) processes. This optimized Au/spinach cathode achieved a high specific area capacity of 7.23 mA‧h/cm2 at a current density of 0.05 mA/cm2 and exhibited excellent stability(280 cycles at 0.05 mA/cm2 with a fixed capacity of 0.2 mA‧h/cm2). The superior performance encourages the construction of more advanced cathode architectures by the use of bio-composites for Li-O2 batteries.
基金financially supported by the National Natural Science Foundation of China(Nos.51872192,51672182 and 51772197)the Thousand Young Talents Plan+2 种基金the Jiangsu Natural Science Foundation(No.BK20180002)the Natural Science Foundation of the Jiangsu Higher Education Institutions of China(Nos.19KJA170001 and 17KJA430013)the Priority Academic Program Development(PAPD)of Jiangsu Higher Education Institutions。
文摘We report a nature-inspired design of tree-like architecture of cuprous oxide(Cu2O)stem and cobalt oxides(CoO)branch serving as an efficient anode for sodium-ion batteries.The construction of stembranch architectures involves the growth of Cu2O nanorods and the subsequent deposition of CoO nanowires.Due to abundant active sites and full exposure to electrolyte,such a Cu2O@CoO stem-branch architecture demonstrates a robust storage toward Na^+ions,affording a capacity retention of^100%over 300 continuous cycles and a remarkable rate capability of 296 mAh g^-1 at 1 A g^-1.This result clearly shows the potential of nature-inspired materials engineering may find extensive applications in the design of high-performance electrodes for rechargeable batteries.
文摘In this paper we provide a novel approach for breaking a significant class of block ciphers, the so-called SPN ciphers, using the process of gene assembly in ciliates. Our proposed scheme utilizes, for the first time, the Turing-powerful potential of gene assembly procedure of ciliated protozoa into the real world computations and has a fewer number of steps than the other proposed schemes to break a cipher. We elaborate notions of formal language theory based on AIR systems, which can be thought of as a modified version of intramolecular scheme to model the ciliate bio-operations, for construction of building blocks necessary for breaking the cipher, and based on these nature-inspired constructions which are as powerful as Turing machines, we propose a theoretical approach for breaking SPN ciphers. Then, we simulate our proposed plan for breaking these ciphers on a sample block cipher based on this structure. Our results show that the proposed scheme has 51.5 percent improvement over the best previously proposed nature-inspired scheme for breaking a cipher.
