MOEA/D with opposition-based learning for multiobjective optimization problem

Neurocomputing

Volumn 146, Issue , 2014, Pages 48-64

  Ma, Xiaoliang ^a,b   Liu, Fang ^a,b   Qi, Yutao ^a,b   Gong, Maoguo ^b   Yin, Minglei ^a,b   Li, Lingling ^b   Jiao, Licheng ^b   Wu, Jianshe ^b  

^a XIDIAN UNIVERSITY   (China)

^b XIDIAN UNIVERSITY   (China)

Author keywords

Decomposition; Evolutionary algorithm; Multi objective optimization; Opposition based learning

Indexed keywords

ALGORITHMS; ARTIFICIAL INTELLIGENCE; DECOMPOSITION; EVOLUTIONARY ALGORITHMS; RANDOM PROCESSES; REINFORCEMENT LEARNING; LEARNING ALGORITHMS; LEARNING SYSTEMS; OPTIMIZATION;

EVOLUTIONARY MULTIOBJECTIVE OPTIMIZATION; EVOLUTIONARY PROCESS; MANY-OBJECTIVE OPTIMIZATIONS; MULTI OBJECTIVE EVOLUTIONARY ALGORITHMS; MULTI-OBJECTIVE OPTIMIZATION PROBLEM; OPPOSITION-BASED LEARNING; PARAMETER SENSITIVITIES; SCALAR OPTIMIZATION;

MULTIOBJECTIVE OPTIMIZATION;

ALGORITHM; ARTICLE; COMPARATIVE STUDY; CONTROLLED STUDY; EVOLUTIONARY ALGORITHM; EXPERIMENTAL DESIGN; EXPERIMENTAL STUDY; HYPERVOLUME METRIC; INFORMATION PROCESSING; INVERTED GENERATIONAL DISTANCE METRIC; MACHINE LEARNING; MULTIOBJECTIVE EVOLUTIONARY ALGORITHM BASED ON DECOMPOSITION; MULTIOBJECTIVE OPTIMIZATION PROBLEM; OPPOSITION BASED LEARNING MULTIOBJECTIVE EVOLUTIONARY ALGORITHM BASED ON DECOMPOSITION; OPPOSITION BASED POPULATION INITIALIZATION; PARETO OPTIMAL SOLUTION; PRIORITY JOURNAL; PROCESS OPTIMIZATION; REPRODUCIBILITY; SENSITIVITY ANALYSIS; STATISTICAL ANALYSIS; STATISTICAL DISTRIBUTION; STATISTICAL PARAMETERS; TCHEBYCHEFF DECOMPOSITION; VALIDATION PROCESS; CALCULATION; DECOMPOSITION; INTERMETHOD COMPARISON; LEARNING ALGORITHM; MATHEMATICAL COMPUTING; MATHEMATICAL PHENOMENA; MULTIOBJECTIVE EVOLUTIONARY ALGORITHM; OPPOSITION BASED LEARNING; POPULATION SIZE; SENSIBILITY; STATISTICAL MODEL;

EID: 84906947832     PISSN: 09252312     EISSN: 18728286     Source Type: Journal    
DOI: 10.1016/j.neucom.2014.04.068     Document Type: Article

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