计算机工程与应用2018，Vol.54Issue(5)：156-164,9.DOI:10.3778/j.issn.1002-8331.1707-0211
多目标灰狼优化算法的改进策略研究
Research on improved strategy for multi-objective grey wolf optimizer
摘要
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 algo-rithms(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.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Key words
Multi-Objective Grey Wolf Optimizer(MOGWO)/survey strategy/control parameter/pareto optimal front/evaluation method of multi-objective optimization分类
信息技术与安全科学引用本文复制引用
崔明朗,杜海文,魏政磊,李聪..多目标灰狼优化算法的改进策略研究[J].计算机工程与应用,2018,54(5):156-164,9.基金项目
国家自然科学基金(No.61601505) （No.61601505）
航空科学基金(No.20155196022) （No.20155196022）
陕西省自然科学基金(No.2016JQ6050). （No.2016JQ6050）
