The dynamic traveling salesman problem(DTSP)is significant in logistics distribution in real-world applications in smart cities,but it is uncertain and difficult to solve.This paper proposes a scheme library-based ant...The dynamic traveling salesman problem(DTSP)is significant in logistics distribution in real-world applications in smart cities,but it is uncertain and difficult to solve.This paper proposes a scheme library-based ant colony optimization(ACO)with a two-optimization(2-opt)strategy to solve the DTSP efficiently.The work is novel and contributes to three aspects:problemmodel,optimization framework,and algorithmdesign.Firstly,in the problem model,traditional DTSP models often consider the change of travel distance between two nodes over time,while this paper focuses on a special DTSP model in that the node locations change dynamically over time.Secondly,in the optimization framework,the ACO algorithm is carried out in an offline optimization and online application framework to efficiently reuse the historical information to help fast respond to the dynamic environment.The framework of offline optimization and online application is proposed due to the fact that the environmental change inDTSPis caused by the change of node location,and therefore the newenvironment is somehowsimilar to certain previous environments.This way,in the offline optimization,the solutions for possible environmental changes are optimized in advance,and are stored in a mode scheme library.In the online application,when an environmental change is detected,the candidate solutions stored in the mode scheme library are reused via ACO to improve search efficiency and reduce computational complexity.Thirdly,in the algorithm design,the ACO cooperates with the 2-opt strategy to enhance search efficiency.To evaluate the performance of ACO with 2-opt,we design two challenging DTSP cases with up to 200 and 1379 nodes and compare them with other ACO and genetic algorithms.The experimental results show that ACO with 2-opt can solve the DTSPs effectively.展开更多
The objective of this research is to examine the use of feature selection and classification methods for distinguishing different types of brain tumors.The brain tumor is characterized by an anomalous proliferation of ...The objective of this research is to examine the use of feature selection and classification methods for distinguishing different types of brain tumors.The brain tumor is characterized by an anomalous proliferation of brain cells that can either be benign or malignant.Most tumors are misdiagnosed due to the variabil-ity and complexity of lesions,which reduces the survival rate in patients.Diagno-sis of brain tumors via computer vision algorithms is a challenging task.Segmentation and classification of brain tumors are currently one of the most essential surgical and pharmaceutical procedures.Traditional brain tumor identi-fication techniques require manual segmentation or handcrafted feature extraction that is error-prone and time-consuming.Hence the proposed research work is mainly focused on medical image processing,which takes Magnetic Resonance Imaging(MRI)images as input and performs preprocessing,segmentation,fea-ture extraction,feature selection,similarity measurement,and classification steps for identifying brain tumors.Initially,the medianfilter is practically applied to the input image to reduce the noise.The graph-cut segmentation technique is used to segment the tumor region.The texture feature is extracted from the output of the segmented image.The extracted feature is selected by using the Ant Colony Opti-mization(ACO)algorithm to improve the performance of the classifier.This prob-abilistic approach is used to solve computing issues.The Euclidean distance is used to calculate the degree of similarity for each extracted feature.The selected feature value is given to the Relevance Vector Machine(RVM)which is a multi-class classification technique.Finally,the tumor is classified as abnormal or nor-mal.The experimental result reveals that the proposed RVM technique gives a better accuracy range of 98.87%when compared to the traditional Support Vector Machine(SVM)technique.展开更多
A relationship between lung transplant success and many features of recipients’/donors has long been studied.However,modeling a robust model of a potential impact on organ transplant success has proved challenging.In...A relationship between lung transplant success and many features of recipients’/donors has long been studied.However,modeling a robust model of a potential impact on organ transplant success has proved challenging.In this study,a hybrid feature selection model was developed based on ant colony opti-mization(ACO)and k-nearest neighbor(kNN)classifier to investigate the rela-tionship between the most defining features of recipients/donors and lung transplant success using data from the United Network of Organ Sharing(UNOS).The proposed ACO-kNN approach explores the features space to identify the representative attributes and classify patients’functional status(i.e.,quality of life)after lung transplantation.The efficacy of the proposed model was verified using 3,684 records and 118 input features from the UNOS.The developed approach examined the reliability and validity of the lung allocation process.The results are promising regarding accuracy prediction to be 91.3%and low computational time,along with better decision capabilities,emphasizing the potential for automatic classification of the lung and other organs allocation pro-cesses.In addition,the proposed model recommends a new perspective on how medical experts and clinicians respond to uncertain and challenging lung alloca-tion strategies.Having such ACO-kNN model,a medical professional can sum-marize information through the proposed method and make decisions for the upcoming transplants to allocate the donor organ.展开更多
In recent years, Japan, and especially rural areas have faced the growing problems of debt-ridden local railway lines along with the population decline and aging population. Therefore, it is best to consider the disco...In recent years, Japan, and especially rural areas have faced the growing problems of debt-ridden local railway lines along with the population decline and aging population. Therefore, it is best to consider the discontinuation of local railway lines and introduce replacement buses to secure the transportation methods of the local people especially in rural areas. Based on the above background, targeting local railway lines that may be discontinued in the near future, appropriate bus stops when provided with potential bus stops were selected, the present study proposed a method that introduces routes for railway replacement buses adopting ant colony optimization (ACO). The improved ACO was designed and developed based on the requirements set concerning the route length, number of turns, road width, accessibility of railway lines and zones without bus stops as well as the constraint conditions concerning the route length, number of turns and zones without bus stops. Original road network data were generated and processed adopting a geographic information systems (GIS), and these are used to search for the optimal route for railway replacement buses adopting the improved ACO concerning the 8 zones on the target railway line (JR Kakogawa line). By comparing the improved ACO with Dijkstra’s algorithm, its relevance was verified and areas needing further improvements were revealed.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
The traveling salesman problem( TSP) is a well-known combinatorial optimization problem as well as an NP-complete problem. A dynamic multi-swarm particle swarm optimization and ant colony optimization( DMPSO-ACO) was ...The traveling salesman problem( TSP) is a well-known combinatorial optimization problem as well as an NP-complete problem. A dynamic multi-swarm particle swarm optimization and ant colony optimization( DMPSO-ACO) was presented for TSP.The DMPSO-ACO combined the exploration capabilities of the dynamic multi-swarm particle swarm optimizer( DMPSO) and the stochastic exploitation of the ant colony optimization( ACO) for solving the traveling salesman problem. In the proposed hybrid algorithm,firstly,the dynamic swarms,rapidity of the PSO was used to obtain a series of sub-optimal solutions through certain iterative times for adjusting the initial allocation of pheromone in ACO. Secondly,the positive feedback and high accuracy of the ACO were employed to solving whole problem. Finally,to verify the effectiveness and efficiency of the proposed hybrid algorithm,various scale benchmark problems were tested to demonstrate the potential of the proposed DMPSO-ACO algorithm. The results show that DMPSO-ACO is better in the search precision,convergence property and has strong ability to escape from the local sub-optima when compared with several other peer algorithms.展开更多
As a typical screening apparatus,the elliptically vibrating screen was extensively employed for the size classification of granular materials.Unremitting efforts have been paid on the improvement of sieving performanc...As a typical screening apparatus,the elliptically vibrating screen was extensively employed for the size classification of granular materials.Unremitting efforts have been paid on the improvement of sieving performance,but the optimization problem was still perplexing the researchers due to the complexity of sieving process.In the present paper,the sieving process of elliptically vibrating screen was numerically simulated based on the Discrete Element Method(DEM).The production quality and the processing capacity of vibrating screen were measured by the screening efficiency and the screening time,respectively.The sieving parameters including the length of semi-major axis,the length ratio of two semi-axes,the vibration frequency,the inclination angle,the vibration direction angle and the motion direction of screen deck were investigated.Firstly,the Gradient Boosting Decision Trees(GBDT)algorithm was adopted in the modelling task of screening data.The trained prediction models with sufficient generalization performance were obtained,and the relative importance of six parameters for both the screening indexes was revealed.After that,a hybrid MACO-GBDT algorithm based on the Ant Colony Optimization(ACO)was proposed for optimizing the sieving performance of vibrating screen.Both the single objective optimization of screening efficiency and the stepwise optimization of screening results were conducted.Ultimately,the reliability of the MACO-GBDT algorithm were examined by the numerical experiments.The optimization strategy provided in this work would be helpful for the parameter design and the performance improvement of vibrating screens.展开更多
The linear consecutive-k-out-of-n:failure(good)(Lin/Con/k/n:F(G))system consists of n interchangeable components that have different reliabilities.These components are arranged in a line path and different component a...The linear consecutive-k-out-of-n:failure(good)(Lin/Con/k/n:F(G))system consists of n interchangeable components that have different reliabilities.These components are arranged in a line path and different component assignments change the system reliability.The optimization of Lin/Con/k/n:F(G)system is to find an optimal component assignment to maximize the system reliability.As the number of components increases,the computation time for this problem increases considerably.In this paper,we propose a Birnbaum importance-based ant colony optimization(BIACO)algorithm to obtain quasi optimal assignments for such problems.We compare its performance using the Birnbaum importance based two-stage approach(BITA)and Birnbaum importancebased genetic local search(BIGLS)algorithm from previous researches.The experimental results show that the BIACO algorithm has a good performance in the optimization of Lin/Con/k/n:F(G)system.展开更多
This article studies the cooperative search-attack mission problem with dynamic targets and threats, and presents a Distributed Intelligent Self-Organized Mission Planning(DISOMP)algorithm for multiple Unmanned Aerial...This article studies the cooperative search-attack mission problem with dynamic targets and threats, and presents a Distributed Intelligent Self-Organized Mission Planning(DISOMP)algorithm for multiple Unmanned Aerial Vehicles(multi-UAV). The DISOMP algorithm can be divided into four modules: a search module designed based on the distributed Ant Colony Optimization(ACO) algorithm, an attack module designed based on the Parallel Approach(PA)scheme, a threat avoidance module designed based on the Dubins Curve(DC) and a communication module designed for information exchange among the multi-UAV system and the dynamic environment. A series of simulations of multi-UAV searching and attacking the moving targets are carried out, in which the search-attack mission completeness, execution efficiency and system suitability of the DISOMP algorithm are analyzed. The simulation results exhibit that the DISOMP algorithm based on online distributed down-top strategy is characterized by good flexibility, scalability and adaptability, in the dynamic targets searching and attacking problem.展开更多
Purpose–The two main purposes of this paper are:first,the development of a new optimization algorithm called GHSACO by incorporating the global-best harmony search(GHS)which is a stochastic optimization algorithm rec...Purpose–The two main purposes of this paper are:first,the development of a new optimization algorithm called GHSACO by incorporating the global-best harmony search(GHS)which is a stochastic optimization algorithm recently developed,with the ant colony optimization(ACO)algorithm.Second,design of a new indirect adaptive recurrent fuzzy-neural controller(IARFNNC)for uncertain nonlinear systems using the developed optimization method(GHSACO)and the concept of the supervisory controller.Design/methodology/approach–The novel optimization method introduces a novel improvization process,which is different from that of the GHS in the following aspects:a modified harmony memory representation and conception.The use of a global random switching mechanism to monitor the choice between the ACO and GHS.An additional memory consideration selection rule using the ACO random proportional transition rule with a pheromone trail update mechanism.The developed optimization method is applied for parametric optimization of all recurrent fuzzy neural networks adaptive controller parameters.In addition,in order to guarantee that the system states are confined to the safe region,a supervisory controller is incorporated into the IARFNNC global structure.Findings–First,to analyze the performance of GHSACO method and shows its effectiveness,some benchmark functions with different dimensions are used.Simulation results demonstrate that it can find significantly better solutions when compared with the Harmony Search(HS),GHS,improved HS(IHS)and conventional ACO algorithm.In addition,simulation results obtained using an example of nonlinear system shows clearly the feasibility and the applicability of the proposed control method and the superiority of the GHSACO method compared to the HS,its variants,particle swarm optimization,and genetic algorithms applied to the same problem.Originality/value–The proposed new GHS algorithm is more efficient than the original HS method and its most known variants IHS and GHS.The proposed control method is applicable to any uncertain nonlinear system belongs in the class of systems treated in this paper.展开更多
基金supported in part by the National Research Foundation of Korea (NRF-2021H1D3A2A01082705).
文摘The dynamic traveling salesman problem(DTSP)is significant in logistics distribution in real-world applications in smart cities,but it is uncertain and difficult to solve.This paper proposes a scheme library-based ant colony optimization(ACO)with a two-optimization(2-opt)strategy to solve the DTSP efficiently.The work is novel and contributes to three aspects:problemmodel,optimization framework,and algorithmdesign.Firstly,in the problem model,traditional DTSP models often consider the change of travel distance between two nodes over time,while this paper focuses on a special DTSP model in that the node locations change dynamically over time.Secondly,in the optimization framework,the ACO algorithm is carried out in an offline optimization and online application framework to efficiently reuse the historical information to help fast respond to the dynamic environment.The framework of offline optimization and online application is proposed due to the fact that the environmental change inDTSPis caused by the change of node location,and therefore the newenvironment is somehowsimilar to certain previous environments.This way,in the offline optimization,the solutions for possible environmental changes are optimized in advance,and are stored in a mode scheme library.In the online application,when an environmental change is detected,the candidate solutions stored in the mode scheme library are reused via ACO to improve search efficiency and reduce computational complexity.Thirdly,in the algorithm design,the ACO cooperates with the 2-opt strategy to enhance search efficiency.To evaluate the performance of ACO with 2-opt,we design two challenging DTSP cases with up to 200 and 1379 nodes and compare them with other ACO and genetic algorithms.The experimental results show that ACO with 2-opt can solve the DTSPs effectively.
文摘The objective of this research is to examine the use of feature selection and classification methods for distinguishing different types of brain tumors.The brain tumor is characterized by an anomalous proliferation of brain cells that can either be benign or malignant.Most tumors are misdiagnosed due to the variabil-ity and complexity of lesions,which reduces the survival rate in patients.Diagno-sis of brain tumors via computer vision algorithms is a challenging task.Segmentation and classification of brain tumors are currently one of the most essential surgical and pharmaceutical procedures.Traditional brain tumor identi-fication techniques require manual segmentation or handcrafted feature extraction that is error-prone and time-consuming.Hence the proposed research work is mainly focused on medical image processing,which takes Magnetic Resonance Imaging(MRI)images as input and performs preprocessing,segmentation,fea-ture extraction,feature selection,similarity measurement,and classification steps for identifying brain tumors.Initially,the medianfilter is practically applied to the input image to reduce the noise.The graph-cut segmentation technique is used to segment the tumor region.The texture feature is extracted from the output of the segmented image.The extracted feature is selected by using the Ant Colony Opti-mization(ACO)algorithm to improve the performance of the classifier.This prob-abilistic approach is used to solve computing issues.The Euclidean distance is used to calculate the degree of similarity for each extracted feature.The selected feature value is given to the Relevance Vector Machine(RVM)which is a multi-class classification technique.Finally,the tumor is classified as abnormal or nor-mal.The experimental result reveals that the proposed RVM technique gives a better accuracy range of 98.87%when compared to the traditional Support Vector Machine(SVM)technique.
文摘A relationship between lung transplant success and many features of recipients’/donors has long been studied.However,modeling a robust model of a potential impact on organ transplant success has proved challenging.In this study,a hybrid feature selection model was developed based on ant colony opti-mization(ACO)and k-nearest neighbor(kNN)classifier to investigate the rela-tionship between the most defining features of recipients/donors and lung transplant success using data from the United Network of Organ Sharing(UNOS).The proposed ACO-kNN approach explores the features space to identify the representative attributes and classify patients’functional status(i.e.,quality of life)after lung transplantation.The efficacy of the proposed model was verified using 3,684 records and 118 input features from the UNOS.The developed approach examined the reliability and validity of the lung allocation process.The results are promising regarding accuracy prediction to be 91.3%and low computational time,along with better decision capabilities,emphasizing the potential for automatic classification of the lung and other organs allocation pro-cesses.In addition,the proposed model recommends a new perspective on how medical experts and clinicians respond to uncertain and challenging lung alloca-tion strategies.Having such ACO-kNN model,a medical professional can sum-marize information through the proposed method and make decisions for the upcoming transplants to allocate the donor organ.
文摘In recent years, Japan, and especially rural areas have faced the growing problems of debt-ridden local railway lines along with the population decline and aging population. Therefore, it is best to consider the discontinuation of local railway lines and introduce replacement buses to secure the transportation methods of the local people especially in rural areas. Based on the above background, targeting local railway lines that may be discontinued in the near future, appropriate bus stops when provided with potential bus stops were selected, the present study proposed a method that introduces routes for railway replacement buses adopting ant colony optimization (ACO). The improved ACO was designed and developed based on the requirements set concerning the route length, number of turns, road width, accessibility of railway lines and zones without bus stops as well as the constraint conditions concerning the route length, number of turns and zones without bus stops. Original road network data were generated and processed adopting a geographic information systems (GIS), and these are used to search for the optimal route for railway replacement buses adopting the improved ACO concerning the 8 zones on the target railway line (JR Kakogawa line). By comparing the improved ACO with Dijkstra’s algorithm, its relevance was verified and areas needing further improvements were revealed.
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National Natural Science Foundation of China(No.70971020)the Subject of Ministry of Education of Hunan Province,China(No.13C818)+3 种基金the Project of Industrial Science and Technology Support of Hengyang City,Hunan Province,China(No.2013KG63)the Open Project Program of Artificial Intelligence Key Laboratory of Sichuan Province,Sichuan University of Science and Engineering,China(No.2012RYJ03)the Fund Project of Humanities and Social Sciences,Ministry of Education of China(No.13YJCZH147)the Special Fund for Shanghai Colleges' Outstanding Young Teachers' Scientific Research Projects,China(No.ZZGJD12033)
文摘The traveling salesman problem( TSP) is a well-known combinatorial optimization problem as well as an NP-complete problem. A dynamic multi-swarm particle swarm optimization and ant colony optimization( DMPSO-ACO) was presented for TSP.The DMPSO-ACO combined the exploration capabilities of the dynamic multi-swarm particle swarm optimizer( DMPSO) and the stochastic exploitation of the ant colony optimization( ACO) for solving the traveling salesman problem. In the proposed hybrid algorithm,firstly,the dynamic swarms,rapidity of the PSO was used to obtain a series of sub-optimal solutions through certain iterative times for adjusting the initial allocation of pheromone in ACO. Secondly,the positive feedback and high accuracy of the ACO were employed to solving whole problem. Finally,to verify the effectiveness and efficiency of the proposed hybrid algorithm,various scale benchmark problems were tested to demonstrate the potential of the proposed DMPSO-ACO algorithm. The results show that DMPSO-ACO is better in the search precision,convergence property and has strong ability to escape from the local sub-optima when compared with several other peer algorithms.
基金The research work is financially supported by National Natural Science Foundation of China(No.51775113)Natural Science Foundation of Fujian Province(No.2017J01675)+2 种基金51st Scientific Research Fund Program of Fujian University of Technology(No.GY-Z160139)Key Research Platform of NC Equipment and Technology in Fujian Province(No.2014H2002)Subsidized Project for Postgraduates’Innovative Fund in Scientific Research of Huaqiao University(No.17013080007).
文摘As a typical screening apparatus,the elliptically vibrating screen was extensively employed for the size classification of granular materials.Unremitting efforts have been paid on the improvement of sieving performance,but the optimization problem was still perplexing the researchers due to the complexity of sieving process.In the present paper,the sieving process of elliptically vibrating screen was numerically simulated based on the Discrete Element Method(DEM).The production quality and the processing capacity of vibrating screen were measured by the screening efficiency and the screening time,respectively.The sieving parameters including the length of semi-major axis,the length ratio of two semi-axes,the vibration frequency,the inclination angle,the vibration direction angle and the motion direction of screen deck were investigated.Firstly,the Gradient Boosting Decision Trees(GBDT)algorithm was adopted in the modelling task of screening data.The trained prediction models with sufficient generalization performance were obtained,and the relative importance of six parameters for both the screening indexes was revealed.After that,a hybrid MACO-GBDT algorithm based on the Ant Colony Optimization(ACO)was proposed for optimizing the sieving performance of vibrating screen.Both the single objective optimization of screening efficiency and the stepwise optimization of screening results were conducted.Ultimately,the reliability of the MACO-GBDT algorithm were examined by the numerical experiments.The optimization strategy provided in this work would be helpful for the parameter design and the performance improvement of vibrating screens.
基金the National Natural Science Foundation of China(Nos.71871181 and 71471147)the Overseas Expertise Introduction Project for Discipline Innovation(No.B13044)the Top International University Visiting Program for Outstanding Young Scholars of Northwestern Polytechnical University(No.201806295008)。
文摘The linear consecutive-k-out-of-n:failure(good)(Lin/Con/k/n:F(G))system consists of n interchangeable components that have different reliabilities.These components are arranged in a line path and different component assignments change the system reliability.The optimization of Lin/Con/k/n:F(G)system is to find an optimal component assignment to maximize the system reliability.As the number of components increases,the computation time for this problem increases considerably.In this paper,we propose a Birnbaum importance-based ant colony optimization(BIACO)algorithm to obtain quasi optimal assignments for such problems.We compare its performance using the Birnbaum importance based two-stage approach(BITA)and Birnbaum importancebased genetic local search(BIGLS)algorithm from previous researches.The experimental results show that the BIACO algorithm has a good performance in the optimization of Lin/Con/k/n:F(G)system.
基金supported in part by National Natural Science Foundation of China (Nos. 61741313, 61673209, and 61533008)Jiangsu Six Peak of Talents Program, China (No. KTHY-027)Postgraduate Research & Practice Innovation Program of Jiangsu Province, China (No. KYCX18_0303)
文摘This article studies the cooperative search-attack mission problem with dynamic targets and threats, and presents a Distributed Intelligent Self-Organized Mission Planning(DISOMP)algorithm for multiple Unmanned Aerial Vehicles(multi-UAV). The DISOMP algorithm can be divided into four modules: a search module designed based on the distributed Ant Colony Optimization(ACO) algorithm, an attack module designed based on the Parallel Approach(PA)scheme, a threat avoidance module designed based on the Dubins Curve(DC) and a communication module designed for information exchange among the multi-UAV system and the dynamic environment. A series of simulations of multi-UAV searching and attacking the moving targets are carried out, in which the search-attack mission completeness, execution efficiency and system suitability of the DISOMP algorithm are analyzed. The simulation results exhibit that the DISOMP algorithm based on online distributed down-top strategy is characterized by good flexibility, scalability and adaptability, in the dynamic targets searching and attacking problem.
文摘Purpose–The two main purposes of this paper are:first,the development of a new optimization algorithm called GHSACO by incorporating the global-best harmony search(GHS)which is a stochastic optimization algorithm recently developed,with the ant colony optimization(ACO)algorithm.Second,design of a new indirect adaptive recurrent fuzzy-neural controller(IARFNNC)for uncertain nonlinear systems using the developed optimization method(GHSACO)and the concept of the supervisory controller.Design/methodology/approach–The novel optimization method introduces a novel improvization process,which is different from that of the GHS in the following aspects:a modified harmony memory representation and conception.The use of a global random switching mechanism to monitor the choice between the ACO and GHS.An additional memory consideration selection rule using the ACO random proportional transition rule with a pheromone trail update mechanism.The developed optimization method is applied for parametric optimization of all recurrent fuzzy neural networks adaptive controller parameters.In addition,in order to guarantee that the system states are confined to the safe region,a supervisory controller is incorporated into the IARFNNC global structure.Findings–First,to analyze the performance of GHSACO method and shows its effectiveness,some benchmark functions with different dimensions are used.Simulation results demonstrate that it can find significantly better solutions when compared with the Harmony Search(HS),GHS,improved HS(IHS)and conventional ACO algorithm.In addition,simulation results obtained using an example of nonlinear system shows clearly the feasibility and the applicability of the proposed control method and the superiority of the GHSACO method compared to the HS,its variants,particle swarm optimization,and genetic algorithms applied to the same problem.Originality/value–The proposed new GHS algorithm is more efficient than the original HS method and its most known variants IHS and GHS.The proposed control method is applicable to any uncertain nonlinear system belongs in the class of systems treated in this paper.
