耦合横纵向个体更新策略的改进MVO算法
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摘要
为提高多元宇宙优化算法(MVO)的全局探索和局部开采性能,提出一种耦合横纵向个体更新策略的改进MVO算法(IMVO).横向更新策略是建立在宇宙种群层级的一种水平迁移进化机制,通过引入加权学习因子保证子代个体同时向多个父代宇宙继承位置信息,以改善种群的个体多样性和算法全局探索性能,适定性修正虫洞存在概率表达以保证种群个体间的充分信息交互;纵向更新策略是基于宇宙个体层级的一种纵向自我学习进化机制,根据最优宇宙历史信息,通过模拟认知的历史遗忘记忆特性实现记忆均值邻域的再开采,以增强算法局部开采性能.最后通过数值实验验证不同加权学习因子函数对算法性能的差异性影响,改进算法的优化性能和算法稳健性等.
To enhance global exploration and local exploitation performance of the multi verse optimizer(MVO), the improved multi verse optimizer(IMVO) is proposed by coupling horizontal and vertical individual updated strategies. The horizontal updated strategy is a horizontal migration evolution mechanism on the population level, in which population diversification and global exploration performance can be improved by introducing the weighted learning factor(WLF) to guarantee that offspring individuals inherit position information from multiple universes of parent generation simultaneously. The mathematical expression of wormhole existence probability is amended properly to heighten information exchange among individuals. Moreover, the vertical updated strategy is a vertical self-learning evolution mechanism on individual level, in which local exploitation performance can be modified by simulating the history forgotten memory characteristic of human cognition to ensure that the neighbourhood of memory mean position based on history information of best universes is re-exploited. Experimental results verify the performance influence of difference WLF functions on the IMVO, and the better optimization performance and robustness of the proposed algorithm.
To enhance global exploration and local exploitation performance of the multi verse optimizer(MVO), the improved multi verse optimizer(IMVO) is proposed by coupling horizontal and vertical individual updated strategies. The horizontal updated strategy is a horizontal migration evolution mechanism on the population level, in which population diversification and global exploration performance can be improved by introducing the weighted learning factor(WLF) to guarantee that offspring individuals inherit position information from multiple universes of parent generation simultaneously. The mathematical expression of wormhole existence probability is amended properly to heighten information exchange among individuals. Moreover, the vertical updated strategy is a vertical self-learning evolution mechanism on individual level, in which local exploitation performance can be modified by simulating the history forgotten memory characteristic of human cognition to ensure that the neighbourhood of memory mean position based on history information of best universes is re-exploited. Experimental results verify the performance influence of difference WLF functions on the IMVO, and the better optimization performance and robustness of the proposed algorithm.
引文
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[10]Abedinpourshotorban H,Shamsuddin S M,Beheshti Z,et al.Electromagnetic field optimization:A physics-inspired metaheuristic optimization algorithm[J].Swarm and Evolutionary Computation,2016,26:8-22.
[11]Goncalves M S,Lopez R H,Miguel L F F.Search group algorithm:A new metaheuristic method for the optimization of truss structures[J].Computers&Structures,2015,153:165-184.
[12]Askarzadeh A.A novel metaheuristic method for solving constrained engineering optimization problems:Crow search algorithm[J].Computers&Structures,2016,169:1-12.
[13]Mirjalili S.Dragonfly algorithm:A new meta-heuristic optimization technique for solving single-objective,discrete,and multi-objective problems[J].Neural Computing and Applications,2016,27(4):1053-1073.
[14]Mirjalili S,Mirjalili S M,Hatamlou A.Multi-verse optimizer:A nature-inspired algorithm for global optimization[J].Neural Computing and Applications,2016,27(2):495-513.
[15]Faris H,Aljarah I,Mirjalili S.Training feedforward neural networks using multi-verse optimizer for binary classification problems[J].Applied Intelligence,2016,45(2):322-332.
[16]Mirjalili S.Dragonfly algorithm:A new meta-heuristic optimization technique for solving single-objective,discrete,and multi-objective problems[J].Neural Computing and Applications,2016,27(4):1053-1073.
[17]刘爱国,崔宜兰.倒S型曲线模型的研究[J].工科数学,1996,12(2):21-24.(Liu A G,Cui Y L.Research on curve model of inverse S[J].J of Mathematics for Technology,1996,12(2):21-24.)
[18]唐若笠,方彦军,孔政敏.一种深度扩展记忆的仿人粒子群算法仿真分析[J].控制与决策,2015,30(4):630-634.(Tang R L,Fang Y J,Kong Z M.Simulation analysis of human simulated PSO based on deep extended memory[J].Control and Decision,2015,30(4):630-634.)