Artificial bee colony(ABC) is one of the most popular swarm intelligence optimization algorithms which have been widely used in numerical optimization and engineering applications. However, there are still deficiencie...Artificial bee colony(ABC) is one of the most popular swarm intelligence optimization algorithms which have been widely used in numerical optimization and engineering applications. However, there are still deficiencies in ABC regarding its local search ability and global search efficiency. Aiming at these deficiencies,an ABC variant named hybrid ABC(HABC) algorithm is proposed.Firstly, the variable neighborhood search factor is added to the solution search equation, which can enhance the local search ability and increase the population diversity. Secondly, inspired by the neuroscience investigation of real honeybees, the memory mechanism is put forward, which assumes the artificial bees can remember their past successful experiences and further guide the subsequent foraging behavior. The proposed memory mechanism is used to improve the global search efficiency. Finally, the results of comparison on a set of ten benchmark functions demonstrate the superiority of HABC.展开更多
Swarm intelligence algorithms are a subset of the artificial intelligence(AI)field,which is increasing popularity in resolving different optimization problems and has been widely utilized in various applications.In th...Swarm intelligence algorithms are a subset of the artificial intelligence(AI)field,which is increasing popularity in resolving different optimization problems and has been widely utilized in various applications.In the past decades,numerous swarm intelligence algorithms have been developed,including ant colony optimization(ACO),particle swarm optimization(PSO),artificial fish swarm(AFS),bacterial foraging optimization(BFO),and artificial bee colony(ABC).This review tries to review the most representative swarm intelligence algorithms in chronological order by highlighting the functions and strengths from 127 research literatures.It provides an overview of the various swarm intelligence algorithms and their advanced developments,and briefly provides the description of their successful applications in optimization problems of engineering fields.Finally,opinions and perspectives on the trends and prospects in this relatively new research domain are represented to support future developments.展开更多
Slope failures lead to catastrophic consequences in numerous countries and thus the stability assessment for slopes is of high interest in geotechnical and geological engineering researches.A hybrid stacking ensemble ...Slope failures lead to catastrophic consequences in numerous countries and thus the stability assessment for slopes is of high interest in geotechnical and geological engineering researches.A hybrid stacking ensemble approach is proposed in this study for enhancing the prediction of slope stability.In the hybrid stacking ensemble approach,we used an artificial bee colony(ABC)algorithm to find out the best combination of base classifiers(level 0)and determined a suitable meta-classifier(level 1)from a pool of 11 individual optimized machine learning(OML)algorithms.Finite element analysis(FEA)was conducted in order to form the synthetic database for the training stage(150 cases)of the proposed model while 107 real field slope cases were used for the testing stage.The results by the hybrid stacking ensemble approach were then compared with that obtained by the 11 individual OML methods using confusion matrix,F1-score,and area under the curve,i.e.AUC-score.The comparisons showed that a significant improvement in the prediction ability of slope stability has been achieved by the hybrid stacking ensemble(AUC?90.4%),which is 7%higher than the best of the 11 individual OML methods(AUC?82.9%).Then,a further comparison was undertaken between the hybrid stacking ensemble method and basic ensemble classifier on slope stability prediction.The results showed a prominent performance of the hybrid stacking ensemble method over the basic ensemble method.Finally,the importance of the variables for slope stability was studied using linear vector quantization(LVQ)method.展开更多
The artificial bee colony （ABC） algorithm is a com- petitive stochastic population-based optimization algorithm. How- ever, the ABC algorithm does not use the social information and lacks the knowledge of the proble...The artificial bee colony （ABC） algorithm is a com- petitive stochastic population-based optimization algorithm. How- ever, the ABC algorithm does not use the social information and lacks the knowledge of the problem structure, which leads to in- sufficiency in both convergent speed and searching precision. Archimedean copula estimation of distribution algorithm （ACEDA） is a relatively simple, time-economic and multivariate correlated EDA. This paper proposes a novel hybrid algorithm based on the ABC algorithm and ACEDA called Archimedean copula estima- tion of distribution based on the artificial bee colony （ACABC） algorithm. The hybrid algorithm utilizes ACEDA to estimate the distribution model and then uses the information to help artificial bees to search more efficiently in the search space. Six bench- mark functions are introduced to assess the performance of the ACABC algorithm on numerical function optimization. Experimen- tal results show that the ACABC algorithm converges much faster with greater precision compared with the ABC algorithm, ACEDA and the global best （gbest）-guided ABC （GABC） algorithm in most of the experiments.展开更多
The distinction and precise identification of tumor nodules are crucial for timely lung cancer diagnosis andplanning intervention. This research work addresses the major issues pertaining to the field of medical image...The distinction and precise identification of tumor nodules are crucial for timely lung cancer diagnosis andplanning intervention. This research work addresses the major issues pertaining to the field of medical imageprocessing while focusing on lung cancer Computed Tomography (CT) images. In this context, the paper proposesan improved lung cancer segmentation technique based on the strengths of nature-inspired approaches. Thebetter resolution of CT is exploited to distinguish healthy subjects from those who have lung cancer. In thisprocess, the visual challenges of the K-means are addressed with the integration of four nature-inspired swarmintelligent techniques. The techniques experimented in this paper are K-means with Artificial Bee Colony (ABC),K-means with Cuckoo Search Algorithm (CSA), K-means with Particle Swarm Optimization (PSO), and Kmeanswith Firefly Algorithm (FFA). The testing and evaluation are performed on Early Lung Cancer ActionProgram (ELCAP) database. The simulation analysis is performed using lung cancer images set against metrics:precision, sensitivity, specificity, f-measure, accuracy,Matthews Correlation Coefficient (MCC), Jaccard, and Dice.The detailed evaluation shows that the K-means with Cuckoo Search Algorithm (CSA) significantly improved thequality of lung cancer segmentation in comparison to the other optimization approaches utilized for lung cancerimages. The results exhibit that the proposed approach (K-means with CSA) achieves precision, sensitivity, and Fmeasureof 0.942, 0.964, and 0.953, respectively, and an average accuracy of 93%. The experimental results prove thatK-meanswithABC,K-meanswith PSO,K-meanswith FFA, andK-meanswithCSAhave achieved an improvementof 10.8%, 13.38%, 13.93%, and 15.7%, respectively, for accuracy measure in comparison to K-means segmentationfor lung cancer images. Further, it is highlighted that the proposed K-means with CSA have achieved a significantimprovement in accuracy, hence can be utilized by researchers for improved segmentation processes of me展开更多
Field penetration index(FPI) is one of the representative key parameters to examine the tunnel boring machine(TBM) performance.Lack of accurate FPI prediction can be responsible for numerous disastrous incidents assoc...Field penetration index(FPI) is one of the representative key parameters to examine the tunnel boring machine(TBM) performance.Lack of accurate FPI prediction can be responsible for numerous disastrous incidents associated with rock mechanics and engineering.This study aims to predict TBM performance(i.e.FPI) by an efficient and improved adaptive neuro-fuzzy inference system(ANFIS) model.This was done using an evolutionary algorithm,i.e.artificial bee colony(ABC) algorithm mixed with the ANFIS model.The role of ABC algorithm in this system is to find the optimum membership functions(MFs) of ANFIS model to achieve a higher degree of accuracy.The procedure and modeling were conducted on a tunnelling database comprising of more than 150 data samples where brittleness index(BI),fracture spacing,α angle between the plane of weakness and the TBM driven direction,and field single cutter load were assigned as model inputs to approximate FPI values.According to the results obtained by performance indices,the proposed ANFISABC model was able to receive the highest accuracy level in predicting FPI values compared with ANFIS model.In terms of coefficient of determination(R^(2)),the values of 0.951 and 0.901 were obtained for training and testing stages of the proposed ANFISABC model,respectively,which confirm its power and capability in solving TBM performance problem.The proposed model can be used in the other areas of rock mechanics and underground space technologies with similar conditions.展开更多
The application of a recent optimization technique,the artificial bee colony(ABC),was investigated in the context of finding the optimal well locations.The ABC performance was compared with the corresponding results f...The application of a recent optimization technique,the artificial bee colony(ABC),was investigated in the context of finding the optimal well locations.The ABC performance was compared with the corresponding results from the particle swarm optimization(PSO)algorithm,under essentially similar conditions.Treatment of out-of-boundary solution vectors was accomplished via the Periodic boundary condition(PBC),which presumably accelerates convergence towards the global optimum.Stochastic searches were initiated from several random staring points,to minimize starting-point dependency in the established results.The optimizations were aimed at maximizing the Net Present Value(NPV)objective function over the considered oilfield production durations.To deal with the issue of reservoir heterogeneity,random permeability was applied via normal/uniform distribution functions.In addition,the issue of increased number of optimization parameters was address,by considering scenarios with multiple injector and producer wells,and cases with deviated wells in a real reservoir model.The typical results prove ABC to excel PSO(in the cases studied)after relatively short optimization cycles,indicating the great premise of ABC methodology to be used for well-optimization purposes.展开更多
It is of great significance for headquarters in warfare to address the weapon-target assignment(WTA)problem with distributed computing nodes to attack targets simultaneously from different weapon units.However,the com...It is of great significance for headquarters in warfare to address the weapon-target assignment(WTA)problem with distributed computing nodes to attack targets simultaneously from different weapon units.However,the computing nodes on the battlefield are vulnerable to be attacked and the communication environment is usually unreliable.To solve the WTA problems in unreliable environments,this paper proposes a scheme based on decentralized peer-to-peer architecture and adapted artificial bee colony(ABC)optimization algorithm.In the decentralized architecture,the peer computing node is distributed to each weapon units and the packet loss rate is used to simulate the unreliable communication environment.The decisions made in each peer node will be merged into the decision set to carry out the optimal decision in the decentralized system by adapted ABC algorithm.The experimental results demonstrate that the decentralized peer-to-peer architecture perform an extraordinary role in the unreliable communication environment.The proposed scheme preforms outstanding results of enemy residual value(ERV)with the packet loss rate in the range from 0 to 0.9.展开更多
Minimum miscibility pressure(MMP)is a key parameter in the successful design of miscible gases injection such as CO2 flooding for enhanced oil recovery process(EOR).MMP is generally determined through experimental tes...Minimum miscibility pressure(MMP)is a key parameter in the successful design of miscible gases injection such as CO2 flooding for enhanced oil recovery process(EOR).MMP is generally determined through experimental tests such as slim tube and rising bubble apparatus(RBA).As these tests are time-consuming and their cost is very expensive,several correlations have been developed.However,and although the simplicity of these correlations,they suffer from inaccuracies and bad generalization due to the limitation of their ranges of application.This paper aims to establish a global model to predict MMP in both pure and impure CO2-crude oil in EOR process by combining support vector regression(SVR)with artificial bee colony(ABC).ABC is used to find best SVR hyper-parameters.201 data collected from authenticated published literature and covering a wide range of variables are considered to develop SVR-ABC pure/impure CO2-crude oil MMP model with following inputs:reservoir temperature(TR),critical temperature of the injection gas(Tc),molecular weight of pentane plus fraction of crude oil(MWC5+)and the ratio of volatile components to intermediate components in crude oil(xvol/xint).Statistical indicators and graphical error analyses show that SVR-ABC MMP model yields excellent results with a low mean absolute percentage error(3.24%)and root mean square error(0.79)and a high coefficient of determination(0.9868).Furthermore,the results reveal that SVR-ABC outperforms either ordinary SVR with trial and error approach or all existing methods considered in this work in the prediction of pure and impure CO2-crude oil MMP.Finally,the Leverage approach(Williams plot)is done to investigate the realm of prediction capability of the new model and to detect any probable erroneous data points.展开更多
Purpose–Artificial bee colony(ABC)algorithm is a relatively new optimization method inspired by the herd behavior of honey bees,which shows quite intelligence.The purpose of this paper is to propose an improved ABC o...Purpose–Artificial bee colony(ABC)algorithm is a relatively new optimization method inspired by the herd behavior of honey bees,which shows quite intelligence.The purpose of this paper is to propose an improved ABC optimization algorithm based on chaos theory for solving the push recovery problem of a quadruped robot,which can tune the controller parameters based on its search mechanism.ADAMS simulation environment is adopted to implement the proposed scheme for the quadruped robot.Design/methodology/approach–Maintaining balance is a rather complicated global optimum problem for a quadruped robot which is about seeking a foot contact point prevents itself from falling down.To ensure the stability of the intelligent robot control system,the intelligent optimization method is employed.The proposed chaotic artificial bee colony(CABC)algorithm is based on basic ABC,and a chaotic mechanism is used to help the algorithm to jump out of the local optimum as well as finding the optimal parameters.The implementation procedure of our proposed chaotic ABC approach is described in detail.Findings–The proposed CABC method is applied to a quadruped robot in ADAMS simulator.Using the CABC to implement,the quadruped robot can work smoothly under the interference.A comparison among the basic ABC and CABC is made.Experimental results verify a better trajectory tracking response can be achieved by the proposed CABC method after control parameters training.Practical implications–The proposed CABC algorithm can be easily applied to practice and can steer the robot during walking,which will considerably increase the autonomy of the robot.Originality/value–The proposed CABC approach is interesting for the optimization of a control scheme for quadruped robot.A parameter training methodology,using the presented intelligent algorithm is proposed to increase the learning capability.The experimental results verify the system stabilization,favorable performance and no chattering phenomena can be achieved by using the proposed CABC a展开更多
基金supported by the National Natural Science Foundation of China(7177121671701209)
文摘Artificial bee colony(ABC) is one of the most popular swarm intelligence optimization algorithms which have been widely used in numerical optimization and engineering applications. However, there are still deficiencies in ABC regarding its local search ability and global search efficiency. Aiming at these deficiencies,an ABC variant named hybrid ABC(HABC) algorithm is proposed.Firstly, the variable neighborhood search factor is added to the solution search equation, which can enhance the local search ability and increase the population diversity. Secondly, inspired by the neuroscience investigation of real honeybees, the memory mechanism is put forward, which assumes the artificial bees can remember their past successful experiences and further guide the subsequent foraging behavior. The proposed memory mechanism is used to improve the global search efficiency. Finally, the results of comparison on a set of ten benchmark functions demonstrate the superiority of HABC.
基金supported in part by the National Natural Science Foundation of China(62073330)in part by the Natural Science Foundation of Hunan Province(2019JJ20021,2020JJ4339)in part by the Scientific Research Fund of Hunan Province Education Department(20B272)。
文摘Swarm intelligence algorithms are a subset of the artificial intelligence(AI)field,which is increasing popularity in resolving different optimization problems and has been widely utilized in various applications.In the past decades,numerous swarm intelligence algorithms have been developed,including ant colony optimization(ACO),particle swarm optimization(PSO),artificial fish swarm(AFS),bacterial foraging optimization(BFO),and artificial bee colony(ABC).This review tries to review the most representative swarm intelligence algorithms in chronological order by highlighting the functions and strengths from 127 research literatures.It provides an overview of the various swarm intelligence algorithms and their advanced developments,and briefly provides the description of their successful applications in optimization problems of engineering fields.Finally,opinions and perspectives on the trends and prospects in this relatively new research domain are represented to support future developments.
基金We acknowledge the funding support from Australia Research Council(Grant Nos.DP200100549 and IH180100010).
文摘Slope failures lead to catastrophic consequences in numerous countries and thus the stability assessment for slopes is of high interest in geotechnical and geological engineering researches.A hybrid stacking ensemble approach is proposed in this study for enhancing the prediction of slope stability.In the hybrid stacking ensemble approach,we used an artificial bee colony(ABC)algorithm to find out the best combination of base classifiers(level 0)and determined a suitable meta-classifier(level 1)from a pool of 11 individual optimized machine learning(OML)algorithms.Finite element analysis(FEA)was conducted in order to form the synthetic database for the training stage(150 cases)of the proposed model while 107 real field slope cases were used for the testing stage.The results by the hybrid stacking ensemble approach were then compared with that obtained by the 11 individual OML methods using confusion matrix,F1-score,and area under the curve,i.e.AUC-score.The comparisons showed that a significant improvement in the prediction ability of slope stability has been achieved by the hybrid stacking ensemble(AUC?90.4%),which is 7%higher than the best of the 11 individual OML methods(AUC?82.9%).Then,a further comparison was undertaken between the hybrid stacking ensemble method and basic ensemble classifier on slope stability prediction.The results showed a prominent performance of the hybrid stacking ensemble method over the basic ensemble method.Finally,the importance of the variables for slope stability was studied using linear vector quantization(LVQ)method.
基金supported by the National Natural Science Foundation of China(61201370)the Special Funding Project for Independent Innovation Achievement Transform of Shandong Province(2012CX30202)the Natural Science Foundation of Shandong Province(ZR2014FM039)
文摘The artificial bee colony （ABC） algorithm is a com- petitive stochastic population-based optimization algorithm. How- ever, the ABC algorithm does not use the social information and lacks the knowledge of the problem structure, which leads to in- sufficiency in both convergent speed and searching precision. Archimedean copula estimation of distribution algorithm （ACEDA） is a relatively simple, time-economic and multivariate correlated EDA. This paper proposes a novel hybrid algorithm based on the ABC algorithm and ACEDA called Archimedean copula estima- tion of distribution based on the artificial bee colony （ACABC） algorithm. The hybrid algorithm utilizes ACEDA to estimate the distribution model and then uses the information to help artificial bees to search more efficiently in the search space. Six bench- mark functions are introduced to assess the performance of the ACABC algorithm on numerical function optimization. Experimen- tal results show that the ACABC algorithm converges much faster with greater precision compared with the ABC algorithm, ACEDA and the global best （gbest）-guided ABC （GABC） algorithm in most of the experiments.
基金the Researchers Supporting Project(RSP2023R395),King Saud University,Riyadh,Saudi Arabia.
文摘The distinction and precise identification of tumor nodules are crucial for timely lung cancer diagnosis andplanning intervention. This research work addresses the major issues pertaining to the field of medical imageprocessing while focusing on lung cancer Computed Tomography (CT) images. In this context, the paper proposesan improved lung cancer segmentation technique based on the strengths of nature-inspired approaches. Thebetter resolution of CT is exploited to distinguish healthy subjects from those who have lung cancer. In thisprocess, the visual challenges of the K-means are addressed with the integration of four nature-inspired swarmintelligent techniques. The techniques experimented in this paper are K-means with Artificial Bee Colony (ABC),K-means with Cuckoo Search Algorithm (CSA), K-means with Particle Swarm Optimization (PSO), and Kmeanswith Firefly Algorithm (FFA). The testing and evaluation are performed on Early Lung Cancer ActionProgram (ELCAP) database. The simulation analysis is performed using lung cancer images set against metrics:precision, sensitivity, specificity, f-measure, accuracy,Matthews Correlation Coefficient (MCC), Jaccard, and Dice.The detailed evaluation shows that the K-means with Cuckoo Search Algorithm (CSA) significantly improved thequality of lung cancer segmentation in comparison to the other optimization approaches utilized for lung cancerimages. The results exhibit that the proposed approach (K-means with CSA) achieves precision, sensitivity, and Fmeasureof 0.942, 0.964, and 0.953, respectively, and an average accuracy of 93%. The experimental results prove thatK-meanswithABC,K-meanswith PSO,K-meanswith FFA, andK-meanswithCSAhave achieved an improvementof 10.8%, 13.38%, 13.93%, and 15.7%, respectively, for accuracy measure in comparison to K-means segmentationfor lung cancer images. Further, it is highlighted that the proposed K-means with CSA have achieved a significantimprovement in accuracy, hence can be utilized by researchers for improved segmentation processes of me
基金supported by the Faculty Development Competitive Research Grant program of Nazarbayev University(Grant No.021220FD5151)。
文摘Field penetration index(FPI) is one of the representative key parameters to examine the tunnel boring machine(TBM) performance.Lack of accurate FPI prediction can be responsible for numerous disastrous incidents associated with rock mechanics and engineering.This study aims to predict TBM performance(i.e.FPI) by an efficient and improved adaptive neuro-fuzzy inference system(ANFIS) model.This was done using an evolutionary algorithm,i.e.artificial bee colony(ABC) algorithm mixed with the ANFIS model.The role of ABC algorithm in this system is to find the optimum membership functions(MFs) of ANFIS model to achieve a higher degree of accuracy.The procedure and modeling were conducted on a tunnelling database comprising of more than 150 data samples where brittleness index(BI),fracture spacing,α angle between the plane of weakness and the TBM driven direction,and field single cutter load were assigned as model inputs to approximate FPI values.According to the results obtained by performance indices,the proposed ANFISABC model was able to receive the highest accuracy level in predicting FPI values compared with ANFIS model.In terms of coefficient of determination(R^(2)),the values of 0.951 and 0.901 were obtained for training and testing stages of the proposed ANFISABC model,respectively,which confirm its power and capability in solving TBM performance problem.The proposed model can be used in the other areas of rock mechanics and underground space technologies with similar conditions.
文摘The application of a recent optimization technique,the artificial bee colony(ABC),was investigated in the context of finding the optimal well locations.The ABC performance was compared with the corresponding results from the particle swarm optimization(PSO)algorithm,under essentially similar conditions.Treatment of out-of-boundary solution vectors was accomplished via the Periodic boundary condition(PBC),which presumably accelerates convergence towards the global optimum.Stochastic searches were initiated from several random staring points,to minimize starting-point dependency in the established results.The optimizations were aimed at maximizing the Net Present Value(NPV)objective function over the considered oilfield production durations.To deal with the issue of reservoir heterogeneity,random permeability was applied via normal/uniform distribution functions.In addition,the issue of increased number of optimization parameters was address,by considering scenarios with multiple injector and producer wells,and cases with deviated wells in a real reservoir model.The typical results prove ABC to excel PSO(in the cases studied)after relatively short optimization cycles,indicating the great premise of ABC methodology to be used for well-optimization purposes.
基金This work was supported by the Foundation for Distinguished Young Scholars of Fujian Agriculture and Forestry University(xjq201809)MOST of Taiwan(107-2623-E-009-006-D).
文摘It is of great significance for headquarters in warfare to address the weapon-target assignment(WTA)problem with distributed computing nodes to attack targets simultaneously from different weapon units.However,the computing nodes on the battlefield are vulnerable to be attacked and the communication environment is usually unreliable.To solve the WTA problems in unreliable environments,this paper proposes a scheme based on decentralized peer-to-peer architecture and adapted artificial bee colony(ABC)optimization algorithm.In the decentralized architecture,the peer computing node is distributed to each weapon units and the packet loss rate is used to simulate the unreliable communication environment.The decisions made in each peer node will be merged into the decision set to carry out the optimal decision in the decentralized system by adapted ABC algorithm.The experimental results demonstrate that the decentralized peer-to-peer architecture perform an extraordinary role in the unreliable communication environment.The proposed scheme preforms outstanding results of enemy residual value(ERV)with the packet loss rate in the range from 0 to 0.9.
文摘Minimum miscibility pressure(MMP)is a key parameter in the successful design of miscible gases injection such as CO2 flooding for enhanced oil recovery process(EOR).MMP is generally determined through experimental tests such as slim tube and rising bubble apparatus(RBA).As these tests are time-consuming and their cost is very expensive,several correlations have been developed.However,and although the simplicity of these correlations,they suffer from inaccuracies and bad generalization due to the limitation of their ranges of application.This paper aims to establish a global model to predict MMP in both pure and impure CO2-crude oil in EOR process by combining support vector regression(SVR)with artificial bee colony(ABC).ABC is used to find best SVR hyper-parameters.201 data collected from authenticated published literature and covering a wide range of variables are considered to develop SVR-ABC pure/impure CO2-crude oil MMP model with following inputs:reservoir temperature(TR),critical temperature of the injection gas(Tc),molecular weight of pentane plus fraction of crude oil(MWC5+)and the ratio of volatile components to intermediate components in crude oil(xvol/xint).Statistical indicators and graphical error analyses show that SVR-ABC MMP model yields excellent results with a low mean absolute percentage error(3.24%)and root mean square error(0.79)and a high coefficient of determination(0.9868).Furthermore,the results reveal that SVR-ABC outperforms either ordinary SVR with trial and error approach or all existing methods considered in this work in the prediction of pure and impure CO2-crude oil MMP.Finally,the Leverage approach(Williams plot)is done to investigate the realm of prediction capability of the new model and to detect any probable erroneous data points.
基金Natural Science Foundation of China(NSFC)under grant#61273054,#60975072 and#60604009National Key Basic Research Program of China under grant#2013CB035503+2 种基金National High Technology Research and Development Program of China(863 Program)under grant#2011AA040902Program for New Century Excellent Talents in University of China under grant#NCET-10-0021Top-Notch Young Talents Program of China,Fundamental Research Funds for the Central Universities of China,and Aeronautical Foundation of China under grant#20115151019.
文摘Purpose–Artificial bee colony(ABC)algorithm is a relatively new optimization method inspired by the herd behavior of honey bees,which shows quite intelligence.The purpose of this paper is to propose an improved ABC optimization algorithm based on chaos theory for solving the push recovery problem of a quadruped robot,which can tune the controller parameters based on its search mechanism.ADAMS simulation environment is adopted to implement the proposed scheme for the quadruped robot.Design/methodology/approach–Maintaining balance is a rather complicated global optimum problem for a quadruped robot which is about seeking a foot contact point prevents itself from falling down.To ensure the stability of the intelligent robot control system,the intelligent optimization method is employed.The proposed chaotic artificial bee colony(CABC)algorithm is based on basic ABC,and a chaotic mechanism is used to help the algorithm to jump out of the local optimum as well as finding the optimal parameters.The implementation procedure of our proposed chaotic ABC approach is described in detail.Findings–The proposed CABC method is applied to a quadruped robot in ADAMS simulator.Using the CABC to implement,the quadruped robot can work smoothly under the interference.A comparison among the basic ABC and CABC is made.Experimental results verify a better trajectory tracking response can be achieved by the proposed CABC method after control parameters training.Practical implications–The proposed CABC algorithm can be easily applied to practice and can steer the robot during walking,which will considerably increase the autonomy of the robot.Originality/value–The proposed CABC approach is interesting for the optimization of a control scheme for quadruped robot.A parameter training methodology,using the presented intelligent algorithm is proposed to increase the learning capability.The experimental results verify the system stabilization,favorable performance and no chattering phenomena can be achieved by using the proposed CABC a
