关键词: 自适应选择策略, 人工蜂群算法, 反学习初始化, 约束优化

Abstract: A Self-Adaptive Artificial Bee Colony（SA-ABC） algorithm is proposed for constrained optimization problem. To make the initial colony scattered evenly on the search area, the opposite learning initialization is employed. For constraint handling, an adaptive selection strategy is designed, which can balance the feasible individuals and infeasible individuals. Furthermore, to improve the optimal ability of SA-ABC, the best-lead search equation is used in onlooker bee phase. To exam the efficiency, SA-ABC algorithm is tested on 13 well-known benchmark test functions, and the experimental results are compared with other state-of-art algorithms. The analyses of the experimental results suggest that the SA-ABC algorithm outperforms or performs similarly to other algorithms.

Key words: adaptive selection strategy, artificial bee colony algorithm, opposite learning initialization, constrained optimization