Chameleon Swarm Algorithm Enhanced by Differential Evolution
Pengxing Cai, Yu Zhang, Zheng Tang, Shangce Gao · 2022 Joint 12th International Conference on Soft Computing and Intelligent Systems and 23rd International Symposium on Advanced Intelligent Systems (SCIS&ISIS) · 2022
The chameleon swarm algorithm (CSA) is a novel meta-heuristic algorithm for solving unconstrained optimization problems. CSA simulates the dynamic behavior of chameleon foraging method to design optimization mechanism, which performs excellent in function optimization. Differential evolution (DE) is an effective and classical stochastic direct search method based on evolutionary strategies, its operators have good convergence. In this paper, the excellent exploration ability of CSA and outstanding convergence speed of DE are taken into consideration. We ingeniously incorporate the differential evolution operator into CSA, and propose hybrid CSADE to solve 30 benchmark optimization functions. To testify the effectiveness of proposed CSADE, we utilize 29 benchmark functions on CEC 2017 to conduct experiments. Through the analysis of statistical results for many experiments, CSADE outperforms selected state-of-the-art algorithms.