A Multi-Objective Cross Entropy Algorithm Based on Elite Chaotic Local Search

Abstract

Cross-Entropy (CE) optimization algorithm, whose characteristics are accurate and robust, has attracted widespread academic attention in recent years. A major drawback of CE algorithm is that it tends to be trapped in local optima. An advanced elite chaotic multi-objective cross entropy (ECCE) algorithm is proposed to enhance the search capability of CE algorithm confronting complex multimodal functions. Compared with the original algorithm, ECCE algorithm selects an elite individual to execute chaotic local search strategy. In the initial stage of algorithm, chaotic local search could explore search space to avoid premature convergence, it could also narrow search region in final stage to accurately locate optimal solution. The ECCE algorithm has been validated by standard test functions, and simulation results show that ECCE algorithm has certain advantages in optimizing multi-peak functions.