A Hybrid Differential Evolution Algorithm for Solving Function Optimization

摘要

One of the key points resulting in the success of differential evolution (DE) is its mechanism of different mutation strategies for generating mutant vectors. In this paper, we also present a novel mutation strategy inspired by the velocity updating scheme of particle swarm optimization (PSO). The proposed approach is called HDE, which conducts the mutation strategy on the global best vector for each generation. Experimental studies on 8 well-known benchmark functions show that HDE outperforms other three compared DE algorithms in most test cases.