Hybrid Differential Evolution Algorithm with Chaos and Generalized Opposition-Based Learning

This paper presents a hybrid differential evolution (DE) algorithm based on chaos and generalized opposition-based learning (GOBL). In this algorithm, GOBL strategy transforms current search space into a new search space with a random probability, which provides more opportunities for the algorithm to find the global optimum. When the GOBL strategy isn't executed, the chaotic operator, like a mutation operator, will be introduced to help the DE to jump out local optima and improve the global convergence rate. Simulation results show that this hybrid DE algorithm can electively enhance the sea-ching efficiency and greatly improve the searching quality.