基于偏好信息的动态引导式多目标寻优策略研究

A Dynamic Guided Multi-objective Optimization Strategy Based on Preference Information

传统的多目标进化算法研究的重点是获得分布在整个Pareto边界上的最优解集,而在现实问题中,决策者只对边界上某些区域分布的解感兴趣.纳入决策者偏好信息的多目标进化算法的研究很有实际意义.因此节约计算资源、快速有效地找到偏好区域的Pareto解集成为其研究的重点.针对该问题,本文提出基于偏好信息的动态引导式多目标寻优策略.该策略通过设置参数ε反映搜索过程中引导区域的动态性,参数控制DM偏好范围.将解与引导区域的距离作为响应选择策略的一个因素,从而有效地获得期望区域内的折衷解.实验结果表明,该算法具有较好的收敛性.

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 υ 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.