关键词: 人工萤火虫算法, 自适应t分布变异, 最优调教变异, 变异控制因子, 全局探索能力, 局部开发能力

Abstract: The convergence speed of Artificial Glowworm Swarm Optimization (AGSO) algorithm declines, even falls into local minimums, when some glowworms gather in non whole extreme points or some glowworms wander around aimlessly. Concerning this problem, an AGSO algorithm based on adaptive t distribution mixed mutation was proposed. Adaptive t distribution mutation and optimization adjustment mutation was introduced into the AGSO algorithm to improve the diversity of glowworm swarm, and prevent the AGSO algorithm from falling into local minimums. Mutation control factor was defined. Combining history status information, the description of adaptive t distribution mixed mutation was given. The mutation method could enhance ability of global exploration and local development. The emulation results of representative test functions and many application examples show that the proposed algorithm is reliable and efficient. Meanwhile, this algorithm is better than tradition algorithm in terms of speed and precision for seeking the optimum.

Key words: Artificial Glowworm Swarm Optimization (AGSO) algorithm, adaptive t distribution mutation, optimization adjustment mutation, mutation control factor, global exploration ability, local development ability