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Article Dans Une Revue Grey System Année : 2016

Embedding Multi-Attribute Decision Making into Evolutionary Optimization to Solve the Many-Objective Combinatorial Optimization Problems

Résumé

Evolutionary Multi-objective optimization is a popular tool to generate a set of finite optimal alternatives, usually called a Pareto-optimal set, for decision making of engineering optimization problems. However, the current evolutionary algorithms using Pareto optimality or modified Pareto optimality as a ranking metric suffer from the decrease of selection pressure and further deterioration of search capability as the number of objectives increases. To tackle these difficulties when facing the Many-objective optimization problems (number of objectives ≥ 4), this paper introduces a method which embeds an integrated Multi-Attribute Decision Making (MADM) model into the evolutionary optimization as a non-Pareto ranking for selection. This method can convert the Many-objective optimization problems into Single-objective optimization problems, which can greatly reduce the computational complexity by limiting the search to the region of user preference and also diminish the decision making difficulty by providing a user-preferred single optimal solution on or near the Pareto-optimal front. The classical Multi-objective traveling salesman problem (MOTSP), which is a template of many discrete combinatorial optimization problems, is selected as illustrative numerical example for verification and demonstration.
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Dates et versions

hal-02381296 , version 1 (26-11-2019)

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  • HAL Id : hal-02381296 , version 1

Citer

Yicha Zhang, Weijun Wang, Alain Bernard. Embedding Multi-Attribute Decision Making into Evolutionary Optimization to Solve the Many-Objective Combinatorial Optimization Problems. Grey System, 2016, 28 (3), pp.124-143. ⟨hal-02381296⟩
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