A neutral mutated operator applied for DE algorithms

Abstract

As an easily used and powerful heuristic search technique based on population, differential evolution (DE) algorithm has been widely used for many optimization and real engineering projects. Similar to other evolutionary algorithms (EA), DE could not avoid from premature convergence due to over concentrated population, which could be called losing population diversity. To improve the performance, a neutral mutation (NM) operator for DE algorithm is proposed. The proposed operator is inspired by neutral theory of molecular evolution, which claims that most mutations are neutral at the level of molecular. The NM operator maintains slightly deleterious trial vectors with a certain probability in the conventional selection operator of DE. At the same time, two control parameters of Neutral Mutation operator are investigated and a dynamic neutral mutation rate tuning strategy is designed. Besides, some of these trial vectors have a chance to be mutated neutrally within the search domain randomly. As a result, the population is diversified with costing negligible function evaluations. Comprehensive experimental results demonstrate that the presented NM operator could improve population diversity to some extent, especially when the population is not divergent at all. Moreover, a real word problem is used to further evaluate NM operator. Also, this operator can be easily used in other EAs to keep population diversity.