关键词: 磷虾群优化算法, 种群初始化, 精英反向学习, 差分变异算子, 局部学习

Abstract: As Krill Herd（KH） optimization algorithm is easy to fall into local optimum and has slow convergence velocity, so the paper presents an effective approach, called Krill Herd optimization algorithm with reverse-learning and local-learning behavior. First of all, the population is initialized by the chaos mapping and reverse learning method. Then, the elite individual generate their opposite solutions by elite opposition-based learning of adaptive learning dimension space according to the iterations, which can effectively maintain the population diversity. Finally, this paper selects the elite group and uses adaptive Lévy flight distribution and improved differential mutation operator to enhance the local learning ability. This novel meta-heuristic method can accelerate the global convergence speed while preserving the robustness of the basic KH algorithm. The experimental results on 8 benchmark functions show that the proposed algorithm achieves significant improvements in the convergence speed and convergence accuracy.

Key words: Krill Herd（KH） algorithm, population initialization, elite opposition-based learning, differential mutation operator, local-learning