A Grid-Based Decomposition for Evolutionary Multiobjective Optimization
Decomposition based multiobjective evolutionary algorithms (MOEAs) decompose a multiobjective optimization problem into a set of scalar objective subproblems and solve them in a collaborative way. Commonly used decomposition approaches are originated from mathematical programming and the direct use of them may not suit MOEAs due to their population-based property. This paper proposes a grid-based decomposition MOEA (G-MOEA/D). A grid has an inherent property of reflecting the information of convergence, diversity, and neighborhood structures among the solutions, which is very suitable for population-based MOEAs. The extensive experiments are conducted to compare G-MOEA/D with other state-of-art MOEAs. The results show that G-MOEA/D is very competitive with or superior to the compared algorithms.
KeywordsEvolutionary multiobjective optimization Decomposition Neighborhood
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