Embedding multi-attribute decision making into evolutionary optimization to solve the many-objective combinatorial optimization problems
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 optimal...
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| Vydané v: | Journal of Grey System Ročník 28; číslo 3; s. 124 |
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| Hlavní autori: | , , |
| Médium: | Journal Article |
| Jazyk: | English |
| Vydavateľské údaje: |
Burnham
Research Information Ltd
01.01.2016
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| Predmet: | |
| ISSN: | 0957-3720, 2396-9040 |
| On-line prístup: | Získať plný text |
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| Shrnutí: | 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. Keywords: Many-objective Optimization; Multi-attribute Decision Making; Grey Theory; Evolutionary Algorithm |
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| Bibliografia: | SourceType-Scholarly Journals-1 ObjectType-General Information-1 content type line 14 |
| ISSN: | 0957-3720 2396-9040 |