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Hybrid multi-objective Bayesian estimation of distribution algorithm: a comparative analysis for the multi-objective knapsack problem

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JOURNAL OF HEURISTICS 2018年第1期24卷 25-47页

作者： Martins, Marcella S. R. Delgado, Myriam R. B. S. Luders, Ricardo Santana, Roberto Goncalves, Richard A. de Almeida, Carolina P. Fed Univ Technol Parana UTFPR Ave Sete Setembro 3165 Curitiba Parana Brazil Univ Basque Country UPV EHU San Sebastian 20018 Spain Midwest State Univ Parana UNICTR Guarapuava PR Brazil

Nowadays, a number of metaheuristics have been developed for efficiently solving multi-objective optimization problems. estimation of distribution algorithms are a special class of metaheuristic that intensively apply probabilistic modeling and, as well as local search methods, are widely used to make the search more efficient. In this paper, we apply a Hybrid multi-objective Bayesian estimation of distribution Algorithm (HMOBEDA) in multi and many objective scenarios by modeling the joint probability of decision variables, objectives, and the configuration parameters of an embedded local search (LS). We analyze the benefits of the online configuration of LS parameters by comparing the proposed approach with LS off-line versions using instances of the multi-objective knapsack problem with two to five and eight objectives. HMOBEDA is also compared with five advanced evolutionary methods using the same instances. Results show that HMOBEDA outperforms the other approaches including those with off-line configuration. HMOBEDA not only provides the best value for hypervolume indicator and IGD metric in most of the cases, but it also computes a very diverse solutions set close to the estimated Pareto front.

关键词： multi-objective estimation of distribution algorithms Probabilistic modeling Local search Hybridization Automatic algorithm configuration

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HMOBEDA: Hybrid multi-objective Bayesian estimation of distribution Algorithm 16

HMOBEDA: Hybrid Multi-objective Bayesian Estimation of Distr...

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Genetic and Evolutionary Computation Conference (GECCO)

作者： Martins, Marcella S. R. Delgado, Myriam R. B. S. Santana, Roberto Luders, Ricardo Goncalves, Richard Aderbal de Almeida, Carolina Paula Fed Univ Technol Parana UTFPR Ave Sete Setembro 3165 Curitiba Parana Brazil Univ Basque Country UPV EHU San Sebastian 20018 Spain Midwest State Univ Parana UNICTR Guarapuava PR Brazil

ISBN: (纸本)9781450342063

Probabilistic modeling of selected solutions and incorporation of local search methods are approaches that can notably improve the results of multi-objective evolutionary algorithms (MOEAs). In the past, these approaches have been jointly applied to multi-objective problems (MOPs) with excellent results. In this paper, we introduce for the first time a joint probabilistic modeling of (1) local search methods with (2) decision variables and (3) the objectives in a framework named HMOBEDA. The proposed approach is compared with six evolutionary methods (including a modified version of NSGA-III, adapted to solve combinatorial optimization) on instances of the multi-objective knapsack problem with 3, 4, and 5 objectives. Results show that HMOBEDA is a competitive approach. It outperforms the other methods according to the hypervolume indicator.

关键词： local search hybridization probabilistic modeling multi-objective estimation of distribution algorithms

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