自动化学报2017，Vol.43Issue(11)：2014-2032,19.DOI:10.16383/j.aas.2017.c160300
一类新型动态多目标鲁棒进化优化方法
A Novel Dynamic Multi-objective Robust Evolutionary Optimization Method
摘要
Abstract
Traditional methods solving dynamic multi-objective optimization problems(DMOPs)often trigger the evo-lution process again to find the Pareto-optimal solutions as soon as new environment appears. This may lead to larger computation and resources costs, even unable to perform the optimum solution in the limited time. Therefore, a novel evolutionary optimization method is proposed looking for dynamic robust Pareto-optimal solution sets, which are the Pareto-optimal solutions for certain environment. They can approximate to the true Pareto fronts in following consecutive dynamic environments along a certain satisfaction threshold,and directly be used as Pareto solutions of these environments so as to reduce the computation cost. Two metrics including time robustness and performance robustness are presented to measure the environmental adaptability of Pareto-optimal solutions. Subsequently,they are transformed into two kinds of robust optimization models. Multi-objective evolutionary algorithm based on decomposition and penalty-parameter less constraint handling method are introduced to form the decomposition-based dynamic multi-objective robust evolutionary optimization method. Especially, a moving average prediction model is adopted to realize multi-dimensional time series prediction of these solutions. In term of eight benchmark functions and two novel metrics,the simulation results indicate that the proposed method can obtain the robust Pareto-optimal solutions meeting the need of decision makers with more average survive time.关键词
动态多目标优化/进化算法/鲁棒Pareto最优解/鲁棒生存时间Key words
Dynamic multi-objective optimization/evolutionary algorithm/robust Pareto optimal solution/robust sur-vival time引用本文复制引用
陈美蓉,郭一楠,巩敦卫,杨振..一类新型动态多目标鲁棒进化优化方法[J].自动化学报,2017,43(11):2014-2032,19.基金项目
国家重点基础研究发展计划(973计划) (2014CB046300),国家自然科学基金(61573361),中国矿业大学创新团队(2015QN003) 资助 Supported by National Basic Research Program of China (973 Program) (2014CB046300), National Natural Science Founda-tion of China (61573361), and Innovation Team of China Uni-versity of Mining and Technology (2015QN003) （973计划）
