关键词: 多目标灰狼算法, 观察策略, 控制参数, Pareto边界, 多目标优化评价方法

Abstract: For the problems of easily falling into local optimum and poor stability of the Multi-Objective Grey Wolf Optimizer（MOGWO）, two improvement strategies are put forward by studying the movement of grey wolf individual at algorithm optimization process： One is adding the “survey process”, the grey wolf individual is endowed with the ability to explore independently and both the efficiency of algorithm and the ability of jumping out the local optimum solution are improved; the other is improving the adjustment strategy of control parameter. The power function is used to replace the linear function to improve the stability of the algorithm. Based on two universal evaluation methods of multi-objective optimization（Generational Distance and Inverted Generational Distance）, 6 different test functions and 3 different algorithms（the original algorithm, the improved algorithm and the Multi-Objective Particle Swarm Optimization algorithm） are compared with the repeat experiments. The experimental results show the effectiveness and feasibility of the AS-MOGWO from efficiency, ability and stability.

Key words: Multi-Objective Grey Wolf Optimizer（MOGWO）, survey strategy, control parameter, pareto optimal front, evaluation method of multi-objective optimization