A Dynamic Guided Multi-objective Optimization Strategy Based on Preference Information
ZHENG Jin-Hua,JIA Yue
Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education,College of Information Engineering,Xiangtan University,Xiangtan 411105
Abstract The focus of the traditional multi-objective evolutionary algorithms is to obtain the optimal solution set distributed in the entire Pareto frontier. However,in reality problems,the decision makers are merely interested in certain regions of the Pareto frontier. Therefore,it is significant to take the preference information of decision-makers into multi-objective evolutionary algorithms. Thus,how to reduce computing resource and obtain Pareto optimal set effectively in preference regions becomes a hot topic in the research. Aiming at the problem,a dynamic heuristic multi-objective optimization strategy is proposed based on the preference information. The parameter ε is adjusted to reflect the dynamics of the guided regions,and another parameter is set to control the size of preference range of DM. The strategy employs the distance between solution set and the guided regions as a factor for selection strategy. The experimental results show the proposed algorithm with this strategy has a good performance especially on the convergence.
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2014, Vol. 27 
