Comparative analysis of multiobjective evolutionary algorithms for random and correlated instances of multiobjective d-dimensional knapsack problems

Ruchit Shah and Patrick Reed

European Journal of Operational Research, 2011, vol. 211, issue 3, 466-479

Abstract: This study analyzes multiobjective d-dimensional knapsack problems (MOd-KP) within a comparative analysis of three multiobjective evolutionary algorithms (MOEAs): the [epsilon]-nondominated sorted genetic algorithm II ([epsilon]-NSGAII), the strength Pareto evolutionary algorithm 2 (SPEA2) and the [epsilon]-nondominated hierarchical Bayesian optimization algorithm ([epsilon]-hBOA). This study contributes new insights into the challenges posed by correlated instances of the MOd-KP that better capture the decision interdependencies often present in real world applications. A statistical performance analysis of the algorithms uses the unary [epsilon]-indicator, the hypervolume indicator and success rate plots to demonstrate their relative effectiveness, efficiency, and reliability for the MOd-KP instances analyzed. Our results indicate that the [epsilon]-hBOA achieves superior performance relative to [epsilon]-NSGAII and SPEA2 with increasing number of objectives, number of decisions, and correlative linkages between the two. Performance of the [epsilon]-hBOA suggests that probabilistic model building evolutionary algorithms have significant promise for expanding the size and scope of challenging multiobjective problems that can be explored.

Keywords: Combinatorial; optimization; Multiobjective; optimization; Knapsack; problem; Probabilistic; model; building; evolutionary; algorithms; Hierarchical; Bayesian; networks (search for similar items in EconPapers)
Date: 2011
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Citations: View citations in EconPapers (7)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:ejores:v:211:y:2011:i:3:p:466-479

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