An efficient Differential Evolution based algorithm for solving multi-objective optimization problems

► An enhanced DE variant (MODEA) is proposed for solving multi-objective optimization problems (MOPs). ► It is a fusion of opposition based learning, random localization and a single population DE structure. ► MODEA introduces a new selection mechanism for generating a well distributed Pareto optima...

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Veröffentlicht in:European journal of operational research Jg. 217; H. 2; S. 404 - 416
Hauptverfasser: Ali, Musrrat, Siarry, Patrick, Pant, Millie
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Amsterdam Elsevier B.V 01.03.2012
Elsevier
Elsevier Sequoia S.A
Schlagworte:
Algorithms
Applied sciences
Benchmarking
Computer Science
Decision theory. Utility theory
Evolution
Evolutionary computation
Exact sciences and technology
Genetic algorithms
Global optimization
Learning
Mathematical analysis
Mathematical problems
Mathematical programming
Multiple objective programming
Mutations
Operational research and scientific management
Operational research. Management science
Operations Research
Opposition-Based Learning
Optimization
Optimization algorithms
Pareto optimality
Pareto optimum
Random localization
Studies
Random localization
Global optimization
Opposition-Based Learning
Evolutionary computation
Multiple objective programming
Pareto optimum
Evolutionary algorithm
Empirical method
Multiobjective programming
Global optimum
Optimization
Objective function
Localization
Algorithm analysis
ISSN:0377-2217, 1872-6860
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Beschreibung
Zusammenfassung:► An enhanced DE variant (MODEA) is proposed for solving multi-objective optimization problems (MOPs). ► It is a fusion of opposition based learning, random localization and a single population DE structure. ► MODEA introduces a new selection mechanism for generating a well distributed Pareto optimal front. ► The efficiency of MODEA is validated on a set of 9 bi-objective and 5 tri-objective problems. ► Modifications/enhancements embedded in DE provides an efficient mechanism for solving MOPs. In the present study, a modified variant of Differential Evolution (DE) algorithm for solving multi-objective optimization problems is presented. The proposed algorithm, named Multi-Objective Differential Evolution Algorithm (MODEA) utilizes the advantages of Opposition-Based Learning for generating an initial population of potential candidates and the concept of random localization in mutation step. Finally, it introduces a new selection mechanism for generating a well distributed Pareto optimal front. The performance of proposed algorithm is investigated on a set of nine bi-objective and five tri-objective benchmark test functions and the results are compared with some recently modified versions of DE for MOPs and some other Multi Objective Evolutionary Algorithms (MOEAs). The empirical analysis of the numerical results shows the efficiency of the proposed algorithm.
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ISSN:0377-2217
1872-6860
DOI:10.1016/j.ejor.2011.09.025