| 引用本文: | 巩敦卫,季新芳.融入偏好的区间高维多目标集合进化优化方法[J].控制理论与应用,2013,30(11):1369~1383.[点击复制] |
| GONG Dun-wei,JI Xin-fang.Optimizing interval higher-dimensional multi-objective problems using set-based evolutionary algorithms incorporated with preferences[J].Control Theory and Technology,2013,30(11):1369~1383.[点击复制] |
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| 融入偏好的区间高维多目标集合进化优化方法 |
| Optimizing interval higher-dimensional multi-objective problems using set-based evolutionary algorithms incorporated with preferences |
| 摘要点击 2469 全文点击 2508 投稿时间：2012-11-16 修订日期：2013-04-23 |
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| DOI编号 10.7641/CTA.2013.21178 |
| 2013,30(11):1369-1383 |
| 中文关键词 进化算法 高维多目标优化 偏好 区间 集合进化 |
| 英文关键词 evolutionary algorithms higher-dimensional multi-objective optimization preference interval set-based evolution |
| 基金项目 国家自然科学基金资助项目(61105063, 61075061); 江苏省自然科学基金资助项目(BK2012566, BK2010186). |
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| 中文摘要 |
| 尽管区间参数高维多目标优化问题普遍存在且非常重要, 但是, 目前求解该问题的方法却很少. 本文提出一种有效解决该问题的集合进化优化方法, 通过在进化过程中融入决策者的偏好, 以得到符合决策者偏好的Pareto解集. 该方法将原优化问题转化为以超体积、不确定度、决策者满意度为新目标的确定型3目标优化问题; 为了求解转化后的优化问题, 采用集合Pareto占优关系比较个体, 并设计融入决策者偏好的延展性测度, 以进一步区分具有相同序值的个体; 此外, 还提出集合变异与重组策略, 以生成高性能的子代种群. 采用4个基准高维多目标优化问题和1个汽车驾驶室设计问题测试所提方法的性能, 并将其与另外3种方法进行对比. 实验结果验证, 该方法能得到收敛性、延展性、不确定度, 以及决策者满意度均衡的Pareto解集. |
| 英文摘要 |
| Multi-objective optimization problems with interval parameters are ubiquitous and important，yet not many effective methods are available for solving them. To solve these problems, we propose a set-based evolutionary algorithm incorporated with decision-maker (DM)’s preferences to obtain a Pareto solution set which satisfies DM’s preferences. In this algorithm, the original optimization problem is first transformed into a tri-objective deterministic optimization problem with three performance indicators: hyper-volume, uncertainty and DM satisfaction. To solve the transformed problem, we employ a set-based Pareto dominance relation to compare different individuals. Individuals with the same rank are distinguished by using a specially designed extension measure incorporating DM’s preferences. Additionally, a set-based mutation and recombination scheme is suggested to generate an offspring with high performance. Four benchmark multiobjective optimization problems and a car cab design problem have been used to evaluate the proposed method; results are compared with those from other three methods. Conclusions indicate that the proposed method can obtain a Pareto solution set with a desirable compromise between the convergence, extension, uncertainty and the DM’s satisfaction. |
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