融合自适应差分进化机制的多目标灰狼优化算法

摘要/Abstract

摘要： 针对灰狼算法易于陷入局部最优问题,提出了一种融合自适应差分进化机制的多目标灰狼优化算法。首先,将外部种群Archive按目标函数值的距离进行分组以避免存储相似个体。其次,设置头狼选择机制,在外部种群中选择头狼。最后,在更新过程中引入差分进化,择优选择下一代灰狼,同时差分进化参数可根据候选解加权目标函数值动态地自适应调整,平衡算法的局部开发与全局探测性能。基于8个多目标测试函数的验证结果表明,提出的多目标灰狼优化算法的收敛性与分布性优于其他3种算法。

关键词: 参数自适应, 差分进化, 多目标优化, 灰狼算法

Abstract: Due to the grey wolf algorithm is easy to fall into local optimum,a multi-objective grey wolf optimization based on adaptive differential evolution mechanism was proposed.Firstly,the external archive is grouped according to the distance of the objective function value to avoid storing similar individuals.Secondly,the selection mechanism of the head wolf is adopted.Finally,differential evolution is introduced into the updating process to select the next generation of grey wolves.At the same time,the parameters of differential evolution are adaptively adjusted according to the objective value of candidate solutions,to balance the local exploitation and the global exploration performance.The experimental results show that the proposed multi-objective grey wolf optimization has better convergence and distribution than the other three algorithms.

Key words: Differential evolution, Grey wolf algorithm, Multi-objective optimization, Parametric adaptation

中图分类号:

TP301.6

赵云涛, 谌竟成, 李维刚. 融合自适应差分进化机制的多目标灰狼优化算法[J]. 计算机科学, 2019, 46(11A): 83-88. https://doi.org/

ZHAO Yun-tao, CHEN Jing-cheng, LI Wei-gang. Multi-objective Grey Wolf Optimization Hybrid Adaptive Differential Evolution Mechanism[J]. Computer Science, 2019, 46(11A): 83-88. https://doi.org/

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