摘要
为解决现有多目标差分进化算法容易陷入局部最优的问题,引入跳跃基因算子,提出基于跳跃基因的多目标差分进化算法。改进现有的多目标差分算法,在传统交叉算子之后执行跳跃基因操作,以保持种群多样性。数值实验结果表明,该算法能较好地解决局部最优问题,在ZDT和DTLZ测试函数集上具有明显优于现有算法的收敛性能。
In order to handle the local optimum problem of the existing multi-objective differential evolution algorithms,the jumping genes operation is introduced and a novel multi-objective differential evolution algorithm based on jumping genes is presented. Different from the existing algorithms, the proposed algorithm performs jumping genes operation after the classical crossover operation, in order to improve the population diversity. Numerical experimental results indicate that the proposed algorithm is capable of dealing with the local optimum problem and exhibits significantly better convergence performance than the existing algorithms on ZDT and DTLZ function tests.
出处
《计算机工程》
CAS
CSCD
北大核心
2016年第4期168-172,共5页
Computer Engineering
基金
广东省对外科技合作基金资助项目(2013B051000060)
广东省教育部产学研结合基金资助重点项目(2011A090200085)
深圳市科技创新委员会基金资助项目(ZYC201105180515A)
关键词
跳跃基因
局部最优
困难问题
多目标差分进化算法
jumping gene
local optimum
difficult problem
multi-objective differential evolution algorithm
