The Equilibrium Optimiser(EO)has been demonstrated to be one of the metaheuristic algorithms that can effectively solve global optimisation problems.Balancing the paradox between exploration and exploitation operation...The Equilibrium Optimiser(EO)has been demonstrated to be one of the metaheuristic algorithms that can effectively solve global optimisation problems.Balancing the paradox between exploration and exploitation operations while enhancing the ability to jump out of the local optimum are two key points to be addressed in EO research.To alleviate these limitations,an EO variant named adaptive elite-guided Equilibrium Optimiser(AEEO)is introduced.Specifically,the adaptive elite-guided search mechanism enhances the balance between exploration and exploitation.The modified mutualism phase reinforces the information interaction among particles and local optima avoidance.The cooperation of these two mechanisms boosts the overall performance of the basic EO.The AEEO is subjected to competitive experiments with state-of-the-art algorithms and modified algorithms on 23 classical benchmark functions and IEE CEC 2017 function test suite.Experimental results demonstrate that AEEO outperforms several well-performing EO variants,DE variants,PSO variants,SSA variants,and GWO variants in terms of convergence speed and accuracy.In addition,the AEEO algorithm is used for the edge server(ES)placement problem in mobile edge computing(MEC)environments.The experimental results show that the author’s approach outperforms the representative approaches compared in terms of access latency and deployment cost.展开更多
基金National Nature Science Foundation of China under-Grant 61461053,Grant 61461054,and Grant 61072079Yunnan Provincial Education Department Scientific Research Fund Project under-Grant 2022Y008+1 种基金Yunnan University’s Research Innovation Fund for Graduate Students under-Grant KC-22222706project of fund YNWR-QNBJ under-Grant 2018-310.
文摘The Equilibrium Optimiser(EO)has been demonstrated to be one of the metaheuristic algorithms that can effectively solve global optimisation problems.Balancing the paradox between exploration and exploitation operations while enhancing the ability to jump out of the local optimum are two key points to be addressed in EO research.To alleviate these limitations,an EO variant named adaptive elite-guided Equilibrium Optimiser(AEEO)is introduced.Specifically,the adaptive elite-guided search mechanism enhances the balance between exploration and exploitation.The modified mutualism phase reinforces the information interaction among particles and local optima avoidance.The cooperation of these two mechanisms boosts the overall performance of the basic EO.The AEEO is subjected to competitive experiments with state-of-the-art algorithms and modified algorithms on 23 classical benchmark functions and IEE CEC 2017 function test suite.Experimental results demonstrate that AEEO outperforms several well-performing EO variants,DE variants,PSO variants,SSA variants,and GWO variants in terms of convergence speed and accuracy.In addition,the AEEO algorithm is used for the edge server(ES)placement problem in mobile edge computing(MEC)environments.The experimental results show that the author’s approach outperforms the representative approaches compared in terms of access latency and deployment cost.
