Wind energy has been widely applied in power generation to alleviate climate problems.The wind turbine layout of a wind farm is a primary factor of impacting power conversion efficiency due to the wake effect that red...Wind energy has been widely applied in power generation to alleviate climate problems.The wind turbine layout of a wind farm is a primary factor of impacting power conversion efficiency due to the wake effect that reduces the power outputs of wind turbines located in downstream.Wind farm layout optimization(WFLO)aims to reduce the wake effect for maximizing the power outputs of the wind farm.Nevertheless,the wake effect among wind turbines increases significantly as the number of wind turbines increases in the wind farm,which severely affect power conversion efficiency.Conventional heuristic algorithms suffer from issues of low solution quality and local optimum for large-scale WFLO under complex wind scenarios.Thus,a chaotic local search-based genetic learning particle swarm optimizer(CGPSO)is proposed to optimize large-scale WFLO problems.CGPSO is tested on four larger-scale wind farms under four complex wind scenarios and compares with eight state-of-the-art algorithms.The experiment results indicate that CGPSO significantly outperforms its competitors in terms of performance,stability,and robustness.To be specific,a success and failure memories-based selection is proposed to choose a chaotic map for chaotic search local.It improves the solution quality.The parameter and search pattern of chaotic local search are also analyzed for WFLO problems.展开更多
In order to solve reliability-redundancy allocation problems more effectively,a new hybrid algorithm named CDEPSO is proposed in this work,which combines particle swarm optimization (PSO) with differential evolution (...In order to solve reliability-redundancy allocation problems more effectively,a new hybrid algorithm named CDEPSO is proposed in this work,which combines particle swarm optimization (PSO) with differential evolution (DE) and a new chaotic local search.In the CDEPSO algorithm,DE provides its best solution to PSO if the best solution obtained by DE is better than that by PSO,while the best solution in the PSO is performed by chaotic local search.To investigate the performance of CDEPSO,four typical reliability-redundancy allocation problems were solved and the results indicate that the convergence speed and robustness of CDEPSO is better than those of PSO and CPSO (a hybrid algorithm which only combines PSO with chaotic local search).And,compared with the other six improved meta-heuristics,CDEPSO also exhibits more robust performance.In addition,a new performance was proposed to more fairly compare CDEPSO with the same six improved meta-heuristics,and CDEPSO algorithm is the best in solving these problems.展开更多
Wireless sensor network(WSN)is an emerging technology which find useful in several application areas such as healthcare,environmentalmonitoring,border surveillance,etc.Several issues that exist in the designing of WSN...Wireless sensor network(WSN)is an emerging technology which find useful in several application areas such as healthcare,environmentalmonitoring,border surveillance,etc.Several issues that exist in the designing of WSN are node localization,coverage,energy efficiency,security,and so on.In spite of the issues,node localization is considered an important issue,which intends to calculate the coordinate points of unknown nodes with the assistance of anchors.The efficiency of the WSN can be considerably influenced by the node localization accuracy.Therefore,this paper presents a modified search and rescue optimization based node localization technique(MSRONLT)forWSN.The major aim of theMSRO-NLT technique is to determine the positioning of the unknown nodes in theWSN.Since the traditional search and rescue optimization(SRO)algorithm suffers from the local optima problemwith an increase in number of iterations,MSRO algorithm is developed by the incorporation of chaotic maps to improvise the diversity of the technique.The application of the concept of chaotic map to the characteristics of the traditional SRO algorithm helps to achieve better exploration ability of the MSRO algorithm.In order to validate the effective node localization performance of the MSRO-NLT algorithm,a set of simulations were performed to highlight the supremacy of the presented model.A detailed comparative results analysis showcased the betterment of the MSRO-NLT technique over the other compared methods in terms of different measures.展开更多
In this work,we propose a real proportional-integral-derivative plus second-order derivative(PIDD2)controller as an efficient controller for vehicle cruise control systems to address the challenging issues related to ...In this work,we propose a real proportional-integral-derivative plus second-order derivative(PIDD2)controller as an efficient controller for vehicle cruise control systems to address the challenging issues related to efficient operation.In this regard,this paper is the first report in the literature demonstrating the implementation of a real PIDD2 controller for controlling the respective system.We construct a novel and efficient metaheuristic algorithm by improving the performance of the Aquila Optimizer via chaotic local search and modified opposition-based learning strategies and use it as an excellently performing tuning mechanism.We also propose a simple yet effective objective function to increase the performance of the proposed algorithm(CmOBL-AO)to adjust the real PIDD2 controller's parameters effectively.We show the CmOBL-AO algorithm to perform better than the differential evolution algorithm,gravitational search algorithm,African vultures optimization,and the Aquila Optimizer using well-known unimodal,multimodal benchmark functions.CEC2019 test suite is also used to perform ablation experiments to reveal the separate contributions of chaotic local search and modified opposition-based learning strategies to the CmOBL-AO algorithm.For the vehicle cruise control system,we confirm the more excellent performance of the proposed method against particle swarm,gray wolf,salp swarm,and original Aquila optimizers using statistical,Wilcoxon signed-rank,time response,robustness,and disturbance rejection analyses.We also use fourteen reported methods in the literature for the vehicle cruise control system to further verify the more promising performance of the CmOBL-AO-based real PIDD2 controller from a wider perspective.The excellent performance of the proposed method is also illustrated through different quality indicators and different operating speeds.Lastly,we also demonstrate the good performing capability of the CmOBL-AO algorithm for real traffic cases.We show the CmOBL-AO-based real PIDD2 controller as the most efficient method to control a vehicle cruise control system.展开更多
基金partially supported by the Japan Society for the Promotion of Science(JSPS)KAKENHI(JP22H03643)Japan Science and Technology Agency(JST)Support for Pioneering Research Initiated by the Next Generation(SPRING)(JPMJSP2145)JST through the Establishment of University Fellowships towards the Creation of Science Technology Innovation(JPMJFS2115)。
文摘Wind energy has been widely applied in power generation to alleviate climate problems.The wind turbine layout of a wind farm is a primary factor of impacting power conversion efficiency due to the wake effect that reduces the power outputs of wind turbines located in downstream.Wind farm layout optimization(WFLO)aims to reduce the wake effect for maximizing the power outputs of the wind farm.Nevertheless,the wake effect among wind turbines increases significantly as the number of wind turbines increases in the wind farm,which severely affect power conversion efficiency.Conventional heuristic algorithms suffer from issues of low solution quality and local optimum for large-scale WFLO under complex wind scenarios.Thus,a chaotic local search-based genetic learning particle swarm optimizer(CGPSO)is proposed to optimize large-scale WFLO problems.CGPSO is tested on four larger-scale wind farms under four complex wind scenarios and compares with eight state-of-the-art algorithms.The experiment results indicate that CGPSO significantly outperforms its competitors in terms of performance,stability,and robustness.To be specific,a success and failure memories-based selection is proposed to choose a chaotic map for chaotic search local.It improves the solution quality.The parameter and search pattern of chaotic local search are also analyzed for WFLO problems.
基金Project(20040533035)supported by the National Research Foundation for the Doctoral Program of Higher Education of ChinaProject(60874070)supported by the National Natural Science Foundation of China
文摘In order to solve reliability-redundancy allocation problems more effectively,a new hybrid algorithm named CDEPSO is proposed in this work,which combines particle swarm optimization (PSO) with differential evolution (DE) and a new chaotic local search.In the CDEPSO algorithm,DE provides its best solution to PSO if the best solution obtained by DE is better than that by PSO,while the best solution in the PSO is performed by chaotic local search.To investigate the performance of CDEPSO,four typical reliability-redundancy allocation problems were solved and the results indicate that the convergence speed and robustness of CDEPSO is better than those of PSO and CPSO (a hybrid algorithm which only combines PSO with chaotic local search).And,compared with the other six improved meta-heuristics,CDEPSO also exhibits more robust performance.In addition,a new performance was proposed to more fairly compare CDEPSO with the same six improved meta-heuristics,and CDEPSO algorithm is the best in solving these problems.
文摘Wireless sensor network(WSN)is an emerging technology which find useful in several application areas such as healthcare,environmentalmonitoring,border surveillance,etc.Several issues that exist in the designing of WSN are node localization,coverage,energy efficiency,security,and so on.In spite of the issues,node localization is considered an important issue,which intends to calculate the coordinate points of unknown nodes with the assistance of anchors.The efficiency of the WSN can be considerably influenced by the node localization accuracy.Therefore,this paper presents a modified search and rescue optimization based node localization technique(MSRONLT)forWSN.The major aim of theMSRO-NLT technique is to determine the positioning of the unknown nodes in theWSN.Since the traditional search and rescue optimization(SRO)algorithm suffers from the local optima problemwith an increase in number of iterations,MSRO algorithm is developed by the incorporation of chaotic maps to improvise the diversity of the technique.The application of the concept of chaotic map to the characteristics of the traditional SRO algorithm helps to achieve better exploration ability of the MSRO algorithm.In order to validate the effective node localization performance of the MSRO-NLT algorithm,a set of simulations were performed to highlight the supremacy of the presented model.A detailed comparative results analysis showcased the betterment of the MSRO-NLT technique over the other compared methods in terms of different measures.
文摘In this work,we propose a real proportional-integral-derivative plus second-order derivative(PIDD2)controller as an efficient controller for vehicle cruise control systems to address the challenging issues related to efficient operation.In this regard,this paper is the first report in the literature demonstrating the implementation of a real PIDD2 controller for controlling the respective system.We construct a novel and efficient metaheuristic algorithm by improving the performance of the Aquila Optimizer via chaotic local search and modified opposition-based learning strategies and use it as an excellently performing tuning mechanism.We also propose a simple yet effective objective function to increase the performance of the proposed algorithm(CmOBL-AO)to adjust the real PIDD2 controller's parameters effectively.We show the CmOBL-AO algorithm to perform better than the differential evolution algorithm,gravitational search algorithm,African vultures optimization,and the Aquila Optimizer using well-known unimodal,multimodal benchmark functions.CEC2019 test suite is also used to perform ablation experiments to reveal the separate contributions of chaotic local search and modified opposition-based learning strategies to the CmOBL-AO algorithm.For the vehicle cruise control system,we confirm the more excellent performance of the proposed method against particle swarm,gray wolf,salp swarm,and original Aquila optimizers using statistical,Wilcoxon signed-rank,time response,robustness,and disturbance rejection analyses.We also use fourteen reported methods in the literature for the vehicle cruise control system to further verify the more promising performance of the CmOBL-AO-based real PIDD2 controller from a wider perspective.The excellent performance of the proposed method is also illustrated through different quality indicators and different operating speeds.Lastly,we also demonstrate the good performing capability of the CmOBL-AO algorithm for real traffic cases.We show the CmOBL-AO-based real PIDD2 controller as the most efficient method to control a vehicle cruise control system.
