关键词: 萤火虫群优化, Chebyshev混沌映射, 优化, 混沌扰动, 动态步长, 自适应搜索

Abstract: According to basic Glowworm Swarm Optimization（GSO） algorithm has lower precision defects, easily falling into local optimum value and slow convergence speed in solving global optimization problems, an improved Chaos and Self-Adaptive Search Glowworm Swarm Optimization（CSAGSO） algorithm based on chaos and self-adaptive search strategy is proposed. By using chaotic search technology to initialize the glowworm population in order to achieve more reasonable and uniformly distributed initial solutions; meanwhile, CSAGSO applies chaos perturbation optimization strategy to disturb some individuals with low fitness values each generation so as to increase the diversity of the population and the global search ability. CSAGSO applies the self-adaptive search strategy with dynamic step size, and updates the static glowworm location in the process of searching the best value, and so accelerates the convergence speed of the algorithm’s early stage, reduces the shock phenomenon of the algorithm’s late stage. Simulation experiment results show that CSAGSO has less parameters and better stability, and is significantly superior to GSO in computational precision and convergence rate.

Key words: Glowworm Swarm Optimization（GSO）, Chebyshev chaotic map, optimization, chaos perturbation, dynamic step, self-adaptive search