An Improved Multiobjective Optimization Evolutionary Algorithm Based on Decomposition for Complex Pareto Fronts

The multiobjective evolutionary algorithm based on decomposition (MOEA/D) has been shown to be very efficient in solving multiobjective optimization problems (MOPs). In practice, the Pareto-optimal front (POF) of many MOPs has complex characteristics. For example, the POF may have a long tail and sh...

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Veröffentlicht in:IEEE transactions on cybernetics Jg. 46; H. 2; S. 421 - 437
Hauptverfasser: Jiang, Shouyong, Yang, Shengxiang
Format: Journal Article
Sprache:Englisch
Veröffentlicht: United States IEEE 01.02.2016
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Algorithms
Decomposition
Disengaging
Evolutionary algorithms
Evolutionary computation
Mathematical models
Mathematical programming
Mopping
Multiobjective evolutionary algorithm (MOEA)
multiobjective evolutionary algorithm based on decomposition (MOEA/D)
multiobjective optimization
niching
Optical fibers
Optimization
Reproduction
Shape
Sociology
Statistics
test problems
Vectors
multiobjective evolutionary algorithm based on decomposition (MOEA/D)
multiobjective optimization
test problems
Multiobjective evolutionary algorithm (MOEA)
niching
ISSN:2168-2267, 2168-2275, 2168-2275
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Zusammenfassung:The multiobjective evolutionary algorithm based on decomposition (MOEA/D) has been shown to be very efficient in solving multiobjective optimization problems (MOPs). In practice, the Pareto-optimal front (POF) of many MOPs has complex characteristics. For example, the POF may have a long tail and sharp peak and disconnected regions, which significantly degrades the performance of MOEA/D. This paper proposes an improved MOEA/D for handling such kind of complex problems. In the proposed algorithm, a two-phase strategy (TP) is employed to divide the whole optimization procedure into two phases. Based on the crowdedness of solutions found in the first phase, the algorithm decides whether or not to delicate computational resources to handle unsolved subproblems in the second phase. Besides, a new niche scheme is introduced into the improved MOEA/D to guide the selection of mating parents to avoid producing duplicate solutions, which is very helpful for maintaining the population diversity when the POF of the MOP being optimized is discontinuous. The performance of the proposed algorithm is investigated on some existing benchmark and newly designed MOPs with complex POF shapes in comparison with several MOEA/D variants and other approaches. The experimental results show that the proposed algorithm produces promising performance on these complex problems.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
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ISSN:2168-2267
2168-2275
2168-2275
DOI:10.1109/TCYB.2015.2403131