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运筹学学报 >

2017 , Vol. 21 >Issue 1: 44 - 54

DOI: https://doi.org/10.15960/j.cnki.issn.1007-6093.2017.01.005

运筹学

改进种群多样性的双变异差分进化算法

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  • 1. 南京工业大学计算机科学与技术学院, 南京 211816 

收稿日期: 2016-02-01

  网络出版日期: 2017-03-15

基金资助

江苏省高校自然科学基金(No. 12KJB510007)

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Differential evolution algorithm with double  mutation strategies for improving population diversity

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  • 1. College of Computer Science and Technology, Nanjing Tech University, Nanjing 211816, China

Received date: 2016-02-01

  Online published: 2017-03-15

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摘要

差分进化算法(DE) 是一种基于种群的启发式随机搜索技术, 对于解决连续性优化问题具有较强的鲁棒性. 然而传统差分进化算法存在种群多样性和收敛速度之间的矛盾, 一种改进种群多样性的双变异差分进化算法(DADE), 通过引入BFS-best 机制(基于排序的可行解选取递减策略)改进变异算子 ``DE/current-to-best'', 将其与 DE/rand/1 构成双变异策略来改善DE算法中种群多样性减少的问题. 同时, 每个个体的控制参数基于排序自适应更新. 最后, 利用多个 CEC 2013 标准测试函数对改进算法进行测试, 实验结果表明, 改进后的算法能有效改善种群多样性, 较好地提高了算法的全局收敛能力和收敛速度.

关键词： 差分进化; 种群多样性; 双变异策略; 排序

本文引用格式

李荣雨, 陈庆倩, 陈菲尔 . 改进种群多样性的双变异差分进化算法[J]. 运筹学学报, 2017 , 21(1) : 44 -54 . DOI: 10.15960/j.cnki.issn.1007-6093.2017.01.005

Abstract

Differential Evolution (DE) is an efficient population-based heuristic stochastic search technique. It is robust for solving continuous optimization problems. However, the discrepancy of population diversity and convergence rate exists in traditional Differential Evolution. In this paper, differential evolution algorithm based on double mutation strategies for improving population diversity (DADE}) was proposed. This algorithm presents a BFS-best mechanism to improve ``current-to-best'', which cooperates with DE/rand/1 to ensure population diversity. Meanwhile, the control parameters of individuals are updated automatically based on ranking. Finally, several benchmark functions in CEC2013 are used to test the proposed algorithm. The simulation results show that DADE can effectively improve population diversity, achieve better global searching ability and a higher convergence rate.

Key words： differential evolution; population diversity; double mutation strategy; ranking

参考文献

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