A decomposition based memetic multi-objective algorithm for continuous multi-objective optimization problem

Multi-objective evolution algorithm based on decomposition (MOEA/D) had been successfully applied into many multi-objective optimization problems, which had gained a lot of attention from the community of evolutionary algorithm(EA) in the past few years. In MOEA/D, a multi-objective optimization pro...

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Veröffentlicht in:The 27th Chinese Control and Decision Conference (2015 CCDC) S. 896 - 900
Hauptverfasser: Na Wang, Hongfeng Wang, Yaping Fu, Dingwei Wang
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 01.05.2015
Schlagworte:
Evolutionary computation
Evolutionary multi-objective optimization
Genetics
Local search
Measurement
Memetic algorithm
Memetics
MOEA/D
Optimization
Resource management
Search problems
ISSN:1948-9439
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Zusammenfassung:Multi-objective evolution algorithm based on decomposition (MOEA/D) had been successfully applied into many multi-objective optimization problems, which had gained a lot of attention from the community of evolutionary algorithm(EA) in the past few years. In MOEA/D, a multi-objective optimization problem would be converted into a set of scalar single-objective subproblems and then utilize EA to address these subproblems simultaneously. In order to further improve its performance, a local search operator, which is designed via the diverse information of neighboring individuals in the search space, and a resource allocation strategy, which is used to balance the trade-off between genetic operator and local search operator, are both introduced into the framework of MOEA/D. A set of experiments are carried out to investigate the strength and weakness of our proposed algorithm on a series of benchmark test problems in comparison with the original MOEA/D.
ISSN:1948-9439
DOI:10.1109/CCDC.2015.7162046