This paper comprehensively analyzes the Manta Ray Foraging Optimization(MRFO)algorithm and its integration into diverse academic fields.Introduced in 2020,the MRFO stands as a novel metaheuristic algorithm,drawing ins...This paper comprehensively analyzes the Manta Ray Foraging Optimization(MRFO)algorithm and its integration into diverse academic fields.Introduced in 2020,the MRFO stands as a novel metaheuristic algorithm,drawing inspiration from manta rays’unique foraging behaviors—specifically cyclone,chain,and somersault foraging.These biologically inspired strategies allow for effective solutions to intricate physical challenges.With its potent exploitation and exploration capabilities,MRFO has emerged as a promising solution for complex optimization problems.Its utility and benefits have found traction in numerous academic sectors.Since its inception in 2020,a plethora of MRFO-based research has been featured in esteemed international journals such as IEEE,Wiley,Elsevier,Springer,MDPI,Hindawi,and Taylor&Francis,as well as at international conference proceedings.This paper consolidates the available literature on MRFO applications,covering various adaptations like hybridized,improved,and other MRFO variants,alongside optimization challenges.Research trends indicate that 12%,31%,8%,and 49%of MRFO studies are distributed across these four categories respectively.展开更多
Fish in nature exhibit a variety of swimming modes such as forward swimming,backward swimming,turning,pitching,etc.,enabling them to swim in complex scenes such as coral reefs.It is still difficult for a robotic fish ...Fish in nature exhibit a variety of swimming modes such as forward swimming,backward swimming,turning,pitching,etc.,enabling them to swim in complex scenes such as coral reefs.It is still difficult for a robotic fish to swim autonomously in a confined area as a real fish.Here,we develop an untethered robotic manta as an experimental platform,which consists of two flexible pectoral fins and a tail fin,with three infrared sensors installed on the front,left,and right sides of the head to sense the surrounding obstacles.To generate multiple swimming modes of the robotic manta and online switching of different modes,we design a closed-loop Central Pattern Generator(CPG)controller based on distance information and use a combination of phase difference and amplitude of the CPG model to achieve stable and rapid adjustment of yaw angle.To verify the autonomous swimming ability of the robotic manta in complex scenes,we design an experimental scenario with a concave obstacle.The experimental results show that the robotic manta can achieve forward swimming,backward swimming,in situ turning within the concave obstacle,and finally exit from the area safely while relying on the perception of external obstacles,which can provide insight into the autonomous exploration of complex scenes by the biomimetic robotic fish.Finally,the swimming ability of the robotic manta is verified by field tests.展开更多
Support Vector Machine(SVM)has become one of the traditional machine learning algorithms the most used in prediction and classification tasks.However,its behavior strongly depends on some parameters,making tuning thes...Support Vector Machine(SVM)has become one of the traditional machine learning algorithms the most used in prediction and classification tasks.However,its behavior strongly depends on some parameters,making tuning these parameters a sensitive step to maintain a good performance.On the other hand,and as any other classifier,the performance of SVM is also affected by the input set of features used to build the learning model,which makes the selection of relevant features an important task not only to preserve a good classification accuracy but also to reduce the dimensionality of datasets.In this paper,the MRFO+SVM algorithm is introduced by investigating the recent manta ray foraging optimizer to fine-tune the SVM parameters and identify the optimal feature subset simultaneously.The proposed approach is validated and compared with four SVM-based algorithms over eight benchmarking datasets.Additionally,it is applied to a disease Covid-19 dataset.The experimental results show the high ability of the proposed algorithm to find the appropriate SVM’s parameters,and its acceptable performance to deal with feature selection problem.展开更多
Bionic manta underwater vehicles will play an essential role in future oceans and can perform tasks,such as long-duration reconnaissance and exploration,due to their efficient propulsion.The manta wings’deformation i...Bionic manta underwater vehicles will play an essential role in future oceans and can perform tasks,such as long-duration reconnaissance and exploration,due to their efficient propulsion.The manta wings’deformation is evident during the swimming process.To improve the propulsion performance of the unmanned submersible,the study of the deformation into the bionic pectoral fin is necessary.In this research,we designed and fabricated a flexible bionic pectoral fin,which is based on the Fin Ray®effect with active and passive deformation(APD)capability.The APD fin was actively controlled by two servo motors and could be passively deformed to variable degrees.The APD fin was moved at 0.5 Hz beat frequency,and the propulsive performance was experimentally verified of the bionic pectoral fins equipped with different extents of deformation.These results showed that the pectoral fin with active–passive deformed capabilities could achieve similar natural biological deformation in the wingspan direction.The average thrust(T)under the optimal wingspan deformation is 61.5%higher than the traditional passive deformed pectoral fins.The obtained results shed light on the design and optimization of the bionic pectoral fins to improve the propulsive performance of unmanned underwater vehicles(UUV).展开更多
In order to play a positive role of decentralised wind power on-grid for voltage stability improvement and loss reduction of distribution network,a multi-objective two-stage decentralised wind power planning method is...In order to play a positive role of decentralised wind power on-grid for voltage stability improvement and loss reduction of distribution network,a multi-objective two-stage decentralised wind power planning method is proposed in the paper,which takes into account the network loss correction for the extreme cold region.Firstly,an electro-thermal model is introduced to reflect the effect of temperature on conductor resistance and to correct the results of active network loss calculation;secondly,a two-stage multi-objective two-stage decentralised wind power siting and capacity allocation and reactive voltage optimisation control model is constructed to take account of the network loss correction,and the multi-objective multi-planning model is established in the first stage to consider the whole-life cycle investment cost of WTGs,the system operating cost and the voltage quality of power supply,and the multi-objective planning model is established in the second stage.planning model,and the second stage further develops the reactive voltage control strategy of WTGs on this basis,and obtains the distribution network loss reduction method based on WTG siting and capacity allocation and reactive power control strategy.Finally,the optimal configuration scheme is solved by the manta ray foraging optimisation(MRFO)algorithm,and the loss of each branch line and bus loss of the distribution network before and after the adoption of this loss reduction method is calculated by taking the IEEE33 distribution system as an example,which verifies the practicability and validity of the proposed method,and provides a reference introduction for decision-making for the distributed energy planning of the distribution network.展开更多
A semi supervised image classification method for satellite images is proposed in this paper.The satellite images contain enormous data that can be used in various applications.The analysis of the data is a tedious ta...A semi supervised image classification method for satellite images is proposed in this paper.The satellite images contain enormous data that can be used in various applications.The analysis of the data is a tedious task due to the amount of data and the heterogeneity of the data.Thus,in this paper,a Radial Basis Function Neural Network(RBFNN)trained using Manta Ray Foraging Optimization algorithm(MRFO)is proposed.RBFNN is a three-layer network comprising of input,output,and hidden layers that can process large amounts.The trained network can discover hidden data patterns in unseen data.The learning algorithm and seed selection play a vital role in the performance of the network.The seed selection is done using the spectral indices to further improve the performance of the network.The manta ray foraging optimization algorithm is inspired by the intelligent behaviour of manta rays.It emulates three unique foraging behaviours namelys chain,cyclone,and somersault foraging.The satellite images contain enormous amount of data and thus require exploration in large search space.The spiral movement of the MRFO algorithm enables it to explore large search spaces effectively.The proposed method is applied on pre and post flooding Landsat 8 Operational Land Imager(OLI)images of New Brunswick area.The method was applied to identify and classify the land cover changes in the area induced by flooding.The images are classified using the proposed method and a change map is developed using post classification comparison.The change map shows that a large amount of agricultural area was washed away due to flooding.The measurement of the affected area in square kilometres is also performed for mitigation activities.The results show that post flooding the area covered by water is increased whereas the vegetated area is decreased.The performance of the proposed method is done with existing state-of-the-art methods.展开更多
In this research paper,an improved strategy to enhance the performance of the DC-link voltage loop regulation in a Doubly Fed Induction Generator(DFIG)based wind energy system has been proposed.The proposed strategy u...In this research paper,an improved strategy to enhance the performance of the DC-link voltage loop regulation in a Doubly Fed Induction Generator(DFIG)based wind energy system has been proposed.The proposed strategy used the robust Fractional-Order(FO)Proportional-Integral(PI)control technique.The FOPI control contains a non-integer order which is preferred over the integer-order control owing to its benefits.It offers extra flexibility in design and demonstrates superior outcomes such as high robustness and effectiveness.The optimal gains of the FOPI controller have been determined using a recent Manta Ray Foraging Optimization(MRFO)algorithm.During the optimization process,the FOPI controller’s parameters are assigned to be the decision variables whereas the objective function is the error racking that to be minimized.To prove the superiority of the MRFO algorithm,an empirical comparison study with the homologous particle swarm optimization and genetic algorithm is achieved.The obtained results proved the superiority of the introduced strategy in tracking and control performances against various conditions such as voltage dips and wind speed variation.展开更多
The biomedical data classification process has received significant attention in recent times due to a massive increase in the generation of healthcare data from various sources.The developments of artificial intellig...The biomedical data classification process has received significant attention in recent times due to a massive increase in the generation of healthcare data from various sources.The developments of artificial intelligence(AI)and machine learning(ML)models assist in the effectual design of medical data classification models.Therefore,this article concentrates on the development of optimal Stacked Long Short Term Memory Sequence-toSequence Autoencoder(OSAE-LSTM)model for biomedical data classification.The presented OSAE-LSTM model intends to classify the biomedical data for the existence of diseases.Primarily,the OSAE-LSTM model involves min-max normalization based pre-processing to scale the data into uniform format.Followed by,the SAE-LSTM model is utilized for the detection and classification of diseases in biomedical data.At last,manta ray foraging optimization(MRFO)algorithm has been employed for hyperparameter optimization process.The utilization of MRFO algorithm assists in optimal selection of hypermeters involved in the SAE-LSTM model.The simulation analysis of the OSAE-LSTM model has been tested using a set of benchmark medical datasets and the results reported the improvements of the OSAELSTM model over the other approaches under several dimensions.展开更多
This paper presents an efficient and versatile OpenFOAM(Open-source Field Operation And Manipulation)-based numerical solver for fully resolved simulations that can handle any rigid and deforming bodies moving in the ...This paper presents an efficient and versatile OpenFOAM(Open-source Field Operation And Manipulation)-based numerical solver for fully resolved simulations that can handle any rigid and deforming bodies moving in the fluid.The algorithm used for solving Fluid-Structure Interactions(FSI)involving the immersed structure with changeable shapes is based on the momentum redistribution method.The present approach excludes the need to solve elastic equations,obtain high-accuracy predictions of the flow field and provide a rigorous basis for implementing the Immersed Boundary Method(IBM).The OpenFOAM implementation of the algorithm is discussed along with the design methodology for developing bio-inspired underwater vehicles using the present solver.The computational results are validated with the experimental observations of the two-dimensional and three-dimensional anguilliform swimmer case studies.The study further extended to the three-dimensional hydrodynamics of a bioinspired,self-propelling manta bot.The motion of the body is specified a priori according to the reported experimental observations.The results quantify the vortex formation and shedding processes and enable the identification of the portions of the body responsible for the majority of thrust.The body accelerates from rest to an asymptotic mean forward velocity of 0.2 ms^(-1)in almost 5 s,consistent with experimental observations.It is observed that the developed computational model is capable of performing any motion simulation and manoeuvrability analysis,which are critical for the designers to develop novel unmanned underwater vehicles.展开更多
油浸式变压器在运行老化过程中难免会出现各种潜伏性故障,及时正确诊断出变压器的状态至关重要,传统利用基于油中溶解气体分析法(dissolved gas analysis, DGA)数据的三比值法因存在编码不足的缺陷,限制了故障的诊断效果。为此提出了一...油浸式变压器在运行老化过程中难免会出现各种潜伏性故障,及时正确诊断出变压器的状态至关重要,传统利用基于油中溶解气体分析法(dissolved gas analysis, DGA)数据的三比值法因存在编码不足的缺陷,限制了故障的诊断效果。为此提出了一种改进的蝠鲼算法(manta ray foraging optimization, MRFO)优化反向传播(back propagation, BP)网络的故障诊断模型。首先利用逻辑映射与反向学习(opposition based learning, OBL)融合的多阶段算法为MRFO提供初始位置,加强算法全局寻优能力;同时提出利用正交实验法优化蝠鲼算法的3种觅食策略,调节蝠鲼个体的探索与开发,以加强该算法在特定问题上的寻优能力;最后将改进的蝠鲼算法寻得的最优解赋予BP网络的权值和偏置,建立变压器故障诊断系统。利用IEC TC 10故障数据进行了实验,并与其他算法进行了结果对比分析。结果表明,所提方法与BPNN、未改进的MRFO-BP、三比值法的结果相比,分别高出16%、8%、24%,是一种积极有效的方法。展开更多
文摘This paper comprehensively analyzes the Manta Ray Foraging Optimization(MRFO)algorithm and its integration into diverse academic fields.Introduced in 2020,the MRFO stands as a novel metaheuristic algorithm,drawing inspiration from manta rays’unique foraging behaviors—specifically cyclone,chain,and somersault foraging.These biologically inspired strategies allow for effective solutions to intricate physical challenges.With its potent exploitation and exploration capabilities,MRFO has emerged as a promising solution for complex optimization problems.Its utility and benefits have found traction in numerous academic sectors.Since its inception in 2020,a plethora of MRFO-based research has been featured in esteemed international journals such as IEEE,Wiley,Elsevier,Springer,MDPI,Hindawi,and Taylor&Francis,as well as at international conference proceedings.This paper consolidates the available literature on MRFO applications,covering various adaptations like hybridized,improved,and other MRFO variants,alongside optimization challenges.Research trends indicate that 12%,31%,8%,and 49%of MRFO studies are distributed across these four categories respectively.
基金supported by the National Key Research and Development Program(Grant No.2020YFB1313200,2022YFC2805200)the National Natural Science Foundation of China(Grant No.52001260,52201381)Ningbo Natural Science Foundation(Grant No.2022J062).
文摘Fish in nature exhibit a variety of swimming modes such as forward swimming,backward swimming,turning,pitching,etc.,enabling them to swim in complex scenes such as coral reefs.It is still difficult for a robotic fish to swim autonomously in a confined area as a real fish.Here,we develop an untethered robotic manta as an experimental platform,which consists of two flexible pectoral fins and a tail fin,with three infrared sensors installed on the front,left,and right sides of the head to sense the surrounding obstacles.To generate multiple swimming modes of the robotic manta and online switching of different modes,we design a closed-loop Central Pattern Generator(CPG)controller based on distance information and use a combination of phase difference and amplitude of the CPG model to achieve stable and rapid adjustment of yaw angle.To verify the autonomous swimming ability of the robotic manta in complex scenes,we design an experimental scenario with a concave obstacle.The experimental results show that the robotic manta can achieve forward swimming,backward swimming,in situ turning within the concave obstacle,and finally exit from the area safely while relying on the perception of external obstacles,which can provide insight into the autonomous exploration of complex scenes by the biomimetic robotic fish.Finally,the swimming ability of the robotic manta is verified by field tests.
文摘Support Vector Machine(SVM)has become one of the traditional machine learning algorithms the most used in prediction and classification tasks.However,its behavior strongly depends on some parameters,making tuning these parameters a sensitive step to maintain a good performance.On the other hand,and as any other classifier,the performance of SVM is also affected by the input set of features used to build the learning model,which makes the selection of relevant features an important task not only to preserve a good classification accuracy but also to reduce the dimensionality of datasets.In this paper,the MRFO+SVM algorithm is introduced by investigating the recent manta ray foraging optimizer to fine-tune the SVM parameters and identify the optimal feature subset simultaneously.The proposed approach is validated and compared with four SVM-based algorithms over eight benchmarking datasets.Additionally,it is applied to a disease Covid-19 dataset.The experimental results show the high ability of the proposed algorithm to find the appropriate SVM’s parameters,and its acceptable performance to deal with feature selection problem.
基金supported by the National Key Research and Development Program(Grant no.2022YFC2805200,2020YFB1313200)the National Natural Science Foundation of China(Grant no.52001260,52201381,52371338)Ningbo Natural Science Foundation(Grant no.2022J062).
文摘Bionic manta underwater vehicles will play an essential role in future oceans and can perform tasks,such as long-duration reconnaissance and exploration,due to their efficient propulsion.The manta wings’deformation is evident during the swimming process.To improve the propulsion performance of the unmanned submersible,the study of the deformation into the bionic pectoral fin is necessary.In this research,we designed and fabricated a flexible bionic pectoral fin,which is based on the Fin Ray®effect with active and passive deformation(APD)capability.The APD fin was actively controlled by two servo motors and could be passively deformed to variable degrees.The APD fin was moved at 0.5 Hz beat frequency,and the propulsive performance was experimentally verified of the bionic pectoral fins equipped with different extents of deformation.These results showed that the pectoral fin with active–passive deformed capabilities could achieve similar natural biological deformation in the wingspan direction.The average thrust(T)under the optimal wingspan deformation is 61.5%higher than the traditional passive deformed pectoral fins.The obtained results shed light on the design and optimization of the bionic pectoral fins to improve the propulsive performance of unmanned underwater vehicles(UUV).
基金supported by the National Natural Science Foundation of China(52177081).
文摘In order to play a positive role of decentralised wind power on-grid for voltage stability improvement and loss reduction of distribution network,a multi-objective two-stage decentralised wind power planning method is proposed in the paper,which takes into account the network loss correction for the extreme cold region.Firstly,an electro-thermal model is introduced to reflect the effect of temperature on conductor resistance and to correct the results of active network loss calculation;secondly,a two-stage multi-objective two-stage decentralised wind power siting and capacity allocation and reactive voltage optimisation control model is constructed to take account of the network loss correction,and the multi-objective multi-planning model is established in the first stage to consider the whole-life cycle investment cost of WTGs,the system operating cost and the voltage quality of power supply,and the multi-objective planning model is established in the second stage.planning model,and the second stage further develops the reactive voltage control strategy of WTGs on this basis,and obtains the distribution network loss reduction method based on WTG siting and capacity allocation and reactive power control strategy.Finally,the optimal configuration scheme is solved by the manta ray foraging optimisation(MRFO)algorithm,and the loss of each branch line and bus loss of the distribution network before and after the adoption of this loss reduction method is calculated by taking the IEEE33 distribution system as an example,which verifies the practicability and validity of the proposed method,and provides a reference introduction for decision-making for the distributed energy planning of the distribution network.
文摘A semi supervised image classification method for satellite images is proposed in this paper.The satellite images contain enormous data that can be used in various applications.The analysis of the data is a tedious task due to the amount of data and the heterogeneity of the data.Thus,in this paper,a Radial Basis Function Neural Network(RBFNN)trained using Manta Ray Foraging Optimization algorithm(MRFO)is proposed.RBFNN is a three-layer network comprising of input,output,and hidden layers that can process large amounts.The trained network can discover hidden data patterns in unseen data.The learning algorithm and seed selection play a vital role in the performance of the network.The seed selection is done using the spectral indices to further improve the performance of the network.The manta ray foraging optimization algorithm is inspired by the intelligent behaviour of manta rays.It emulates three unique foraging behaviours namelys chain,cyclone,and somersault foraging.The satellite images contain enormous amount of data and thus require exploration in large search space.The spiral movement of the MRFO algorithm enables it to explore large search spaces effectively.The proposed method is applied on pre and post flooding Landsat 8 Operational Land Imager(OLI)images of New Brunswick area.The method was applied to identify and classify the land cover changes in the area induced by flooding.The images are classified using the proposed method and a change map is developed using post classification comparison.The change map shows that a large amount of agricultural area was washed away due to flooding.The measurement of the affected area in square kilometres is also performed for mitigation activities.The results show that post flooding the area covered by water is increased whereas the vegetated area is decreased.The performance of the proposed method is done with existing state-of-the-art methods.
文摘In this research paper,an improved strategy to enhance the performance of the DC-link voltage loop regulation in a Doubly Fed Induction Generator(DFIG)based wind energy system has been proposed.The proposed strategy used the robust Fractional-Order(FO)Proportional-Integral(PI)control technique.The FOPI control contains a non-integer order which is preferred over the integer-order control owing to its benefits.It offers extra flexibility in design and demonstrates superior outcomes such as high robustness and effectiveness.The optimal gains of the FOPI controller have been determined using a recent Manta Ray Foraging Optimization(MRFO)algorithm.During the optimization process,the FOPI controller’s parameters are assigned to be the decision variables whereas the objective function is the error racking that to be minimized.To prove the superiority of the MRFO algorithm,an empirical comparison study with the homologous particle swarm optimization and genetic algorithm is achieved.The obtained results proved the superiority of the introduced strategy in tracking and control performances against various conditions such as voltage dips and wind speed variation.
基金The authors extend their appreciation to the Deanship of Scientific Research at King Khalid University for funding this work under grant number(RGP 2/158/43)Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2022R235)Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.The authors would like to thank the Deanship of Scientific Research at Umm Al-Qura University for supporting this work by Grant Code:(22UQU4340237DSR06).
文摘The biomedical data classification process has received significant attention in recent times due to a massive increase in the generation of healthcare data from various sources.The developments of artificial intelligence(AI)and machine learning(ML)models assist in the effectual design of medical data classification models.Therefore,this article concentrates on the development of optimal Stacked Long Short Term Memory Sequence-toSequence Autoencoder(OSAE-LSTM)model for biomedical data classification.The presented OSAE-LSTM model intends to classify the biomedical data for the existence of diseases.Primarily,the OSAE-LSTM model involves min-max normalization based pre-processing to scale the data into uniform format.Followed by,the SAE-LSTM model is utilized for the detection and classification of diseases in biomedical data.At last,manta ray foraging optimization(MRFO)algorithm has been employed for hyperparameter optimization process.The utilization of MRFO algorithm assists in optimal selection of hypermeters involved in the SAE-LSTM model.The simulation analysis of the OSAE-LSTM model has been tested using a set of benchmark medical datasets and the results reported the improvements of the OSAELSTM model over the other approaches under several dimensions.
基金the funding received from Naval Research Board,Marine System Panel to carry out this research work at Shiv Nadar University.Award Number:NRB/4003/PG/400,Recipient:Dr.Santanu Mitra,Ph.D.,Assoc.Professor,Mechanical Engineering Department,Shiv Nadar University.
文摘This paper presents an efficient and versatile OpenFOAM(Open-source Field Operation And Manipulation)-based numerical solver for fully resolved simulations that can handle any rigid and deforming bodies moving in the fluid.The algorithm used for solving Fluid-Structure Interactions(FSI)involving the immersed structure with changeable shapes is based on the momentum redistribution method.The present approach excludes the need to solve elastic equations,obtain high-accuracy predictions of the flow field and provide a rigorous basis for implementing the Immersed Boundary Method(IBM).The OpenFOAM implementation of the algorithm is discussed along with the design methodology for developing bio-inspired underwater vehicles using the present solver.The computational results are validated with the experimental observations of the two-dimensional and three-dimensional anguilliform swimmer case studies.The study further extended to the three-dimensional hydrodynamics of a bioinspired,self-propelling manta bot.The motion of the body is specified a priori according to the reported experimental observations.The results quantify the vortex formation and shedding processes and enable the identification of the portions of the body responsible for the majority of thrust.The body accelerates from rest to an asymptotic mean forward velocity of 0.2 ms^(-1)in almost 5 s,consistent with experimental observations.It is observed that the developed computational model is capable of performing any motion simulation and manoeuvrability analysis,which are critical for the designers to develop novel unmanned underwater vehicles.
文摘油浸式变压器在运行老化过程中难免会出现各种潜伏性故障,及时正确诊断出变压器的状态至关重要,传统利用基于油中溶解气体分析法(dissolved gas analysis, DGA)数据的三比值法因存在编码不足的缺陷,限制了故障的诊断效果。为此提出了一种改进的蝠鲼算法(manta ray foraging optimization, MRFO)优化反向传播(back propagation, BP)网络的故障诊断模型。首先利用逻辑映射与反向学习(opposition based learning, OBL)融合的多阶段算法为MRFO提供初始位置,加强算法全局寻优能力;同时提出利用正交实验法优化蝠鲼算法的3种觅食策略,调节蝠鲼个体的探索与开发,以加强该算法在特定问题上的寻优能力;最后将改进的蝠鲼算法寻得的最优解赋予BP网络的权值和偏置,建立变压器故障诊断系统。利用IEC TC 10故障数据进行了实验,并与其他算法进行了结果对比分析。结果表明,所提方法与BPNN、未改进的MRFO-BP、三比值法的结果相比,分别高出16%、8%、24%,是一种积极有效的方法。
