Hybrid Differential Evolutionary Algorithm Based on Extremal Optimization
Cong Jiao Wang, Xi Huai Wang, Jian Mei Xiao · Applied Mechanics and Materials · 2013
A new hybrid algorithm based on Differential Evolution (DE) and Extremal Optimization (EO) is proposed to solve the premature convergence and low precision of standard differential evolution when applied to complex optimization problems. The key points of it lie in: the hybrid algorithm introduced the population-based Extremal Optimization algorithm in the iteration process of DE when population aggregation got the high degree, which uses the volatility of EO to increase the diversity of population and the ability of breaking away from the local optimum. Simulations show that the hybrid algorithm has remarkable global convergence ability, and can avoid the premature convergence effectively.