A Hybrid Differential Evolution Algorithm Solving Complex Multimodal Optimization Problems

摘要

Differential Evolution (DE) algorithm is a kind of intelligent algorithm based on natural evolution over the past few decades, which shows better optimization performance on some classical benchmark problems. However, it is easy to fall into local optimum and hard to find the global optimal solution when solving complex multimodal optimization problems. In this paper, we propose a Hybrid-strategy-based Differential Evolution (HDE) algorithm. It combines three search strategies, the self-adaptive controlling of parameters, multiple mutation strategy and opposition-based learning. Then we select 11 different types of complex multimodal optimization problems and do experiment with simulated data. The result shows that HDE algorithm gets better performance than other 7 methods.