关键词: 孔群加工路径规划, 多目标优化, 组合优化, 蚁群优化, 人工免疫系统

Abstract: Holes Machining Path Planning is significant to improve the machining efficiency and quality of multi-hole parts. Two mathematical models of Holes Machining Path Planning problems were constructed, which could be induced to single objective and multi-objective combinatorial optimization problems respectively. Two novel evolutionary algorithms, Evolutionary Ant Colony System algorithm and Artificial Immune algorithm, were introduced to solve the single objective combinatorial optimization problems. Analysis indicated that these two algorithms could resist the combinatorial explosion in solution space effectively and had lower computational complexity and higher performance compared with the Hopfield algorithm. By improving the Evolutionary Ant Colony System algorithm and Artificial Immune algorithm with the Technique of Fast Solution Sorting, the Multi-objective Evolutionary Ant Colony System algorithm and Multi-objective Artificial Immune algorithm were proposed. Analysis indicated that the improved algorithms had not increased the computational complexity of the original algorithms and had resolved the multi-objective optimization problem directly without fixing the objective weight vector in advance. In additioiin, it had obtained several Pareto solutions in one run.

Key words: holes machining path planning, multi-objective optimization, combinatorial optimization, ant colony optimization, artificial immune system