Comparison of Multiobjective Evolutionary Algorithms: Empirical Results
Evolutionary Computation
Self-adaptive multimethod search for global optimization in real-parameter spaces
IEEE Transactions on Evolutionary Computation
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
MOEA/D: A Multiobjective Evolutionary Algorithm Based on Decomposition
IEEE Transactions on Evolutionary Computation
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Integration of single methods into their hybrids are researched scarcely in the recent few years. This paper presents the feasibility study for integration of two methods: MOEA/D [7] and NSGA-II [4] in the proposed multimethod search approach (MMTD). During implementation of MMTD, we borrows some concepts from the specialized literature of EMO. In MMTD, the synergetic combination of MOEA/D and NSGA-II can unleash their full power and strength self-adaptively for tackling two set of problems: 1) ZDT test problems [6], 2) cec09 unconstrained test instances [1]. The final best approximated results illustrates the usefulness of MMTD in multiobjective optimization (MO).