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A NOVEL MULTI-OBJECTIVE ESTIMATION OF DISTRIBUTION ALGORITHM based ON sensitivity OF OBJECTIVE FUNCTION

INTERNATIONAL JOURNAL OF INNOVATIVE COMPUTING INFORMATION AND CONTROL 2013年第6期9卷 2543-2566页

作者： Seo, Hyunshik Lee, Chaewoo Ajou Univ Dept Elect & Comp Engn San 5 Suwon 443749 South Korea

Most multi-objective optimization problems (MOPs) have a set of optimal trade-off solutions known as the Pareto-optimal solutions since the objectives in MOPs are usually in conflict with one another. Recently proposed estimation of distribution algorithms (EDAs) build a probability distribution model based on the probabilistic information about decision variables of solutions, and then produce new solutions from the model. In the algorithms, the modeling technique enables the initial large search space to be reduced to small promising solution space during the search. However, the existing EDAs might be inefficient at generating the promising solutions since they depend on the information extracted from the decision variables of current solutions expected to approach the optimal solutions. For effective modeling of the promising solutions, we firstly introduce new information about the relationship between decision variables and objective functions;this information is called sensitivity of objective function. Secondly, we propose a multi-objective estimation of distribution algorithm based on the sensitivity of objective function (MOEDA-S). In the MOEDA-S, the sensitivity-based distribution modeling adapts to the current search strategy such that the convergence-focused search at the beginning part of the search is changed to a diversity-focused search at the latter part of the search. MOEDA-S is compared with two other leading multi-objective evolutionary algorithms on a set of test instances. The simulation results show that MOEDA-S outperforms the two compared algorithms in terms of both convergence and diversity performances of the solutions.

关键词： Multi-objective optimization problems Multi-objective evolutionary algorithms Estimation of distribution algorithms variables sensitivity-based modeling

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