RM-MEDA: A regularity model-based multiobjective estimation of distribution algorithm

被引:726
作者
Zhang, Qingfu [1 ]
Zhou, Aimin [1 ]
Jin, Yaochu [2 ]
机构
[1] Univ Essex, Dept Comp Sci, Colchester CO4 3SQ, Essex, England
[2] Honda Res Inst Europe, D-63073 Offenbach, Germany
关键词
estimation of distribution algorithm; local principal component analysis; multiobjective optimization; regularity scalability; sensitivity; the Karush-Kuhn-Tucker condition; variable linkages;
D O I
10.1109/TEVC.2007.894202
中图分类号
TP18 [人工智能理论];
学科分类号
081104 [模式识别与智能系统]; 0812 [计算机科学与技术]; 0835 [软件工程]; 1405 [智能科学与技术];
摘要
Under mild conditions, it can be induced from the Karush-Kuhn-Tucker condition that the Pareto set, in the decision space, of a continuous multiobjective optimization problem is a piecewise continuous (m - 1)-D manifold, where m Is the number of objectives. Based on this regularity property, we propose a regularity model-based multiobjective estimation of distribution algorithm (RM-MEDA) for continuous multiobjective optimization problems with variable linkages. At each generation, the proposed algorithm models a promising area in the decision space by a probability distribution whose centroid is a (m - 1) -D piecewise continuous manifold. The local principal component analysis algorithm is used for building such a model. New trial solutions are sampled from the model thus built. A nondominated sorting-based selection is used for choosing solutions for the next generation. Systematic experiments have shown that, overall, RM-MEDA outperforms three other state-of-the-art algorithms, namely, GDE3, PCX-NSGA-II, and MIDEA, on a set of test instances with variable linkages. We have demonstrated that, compared with GDE3, RM-MEDA is not sensitive to algorithmic parameters, and has good scalability to the number of decision variables in the case of nonlinear variable linkages. A few shortcomings of RM-MEDA have also been identified and discussed in this paper.
引用
收藏
页码:41 / 63
页数:23
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