DSM-DE: a differential evolution with dynamic speciation-based mutation for single-objective optimization

Libao Deng

doi:10.1007/s12293-019-00279-0

Return

DSM-DE: a differential evolution with dynamic speciation-based mutation for single-objective optimization

2019-01-11

PRE

DOI：10.1007/s12293-019-00279-0

Original

Original request for help

Save

Abstract

En 中文

A new differential evolution algorithm with two dynamic speciation-based mutation strategies (DSM-DE) is proposed to solve single-objective optimization problems. An explorative mutation DE/seeds-to-seeds and an exploitative mutation DE/seeds-to-rand are employed simultaneously in DSM-DE in the evolutionary process. A Dynamic Speciation Technique is designed to assist the two mutations in order to utilize the potential of selective portioning of critical individuals in the population. It dynamically divides the population into numbers of species whilst taking species seeds as centers. The best individuals for each species are used as base vectors in each species in the proposed mutation strategies. DE/seeds-to-seeds selects individuals from species seeds and current species to constitute difference vectors whereas DE/seeds-to-rand selects from the whole population. Thus the two mutation strategies can accelerate the convergence process without decreasing diversity of the population. Comparison results with four classic DE variants, one state-of-art DE variant and two improved non-DE variants on CEC2014, CEC2015 benchmark, and Lennard-Jones potential problem reveal that the overall performance of DSM-DE is better than that of the other seven DE algorithms. In addition, experiments also substantiate the effectiveness and superiority of two seeds-guided mutation strategies in DSM-DE.

Keywords:

Differential evolution

Mutation strategy

Dynamic speciation

Single-objective optimization

AI Summary

Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.