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An adaptive evolutionary multi-objective approach based on simulated annealing

Li, Hui; Landa-Silva, Dario

Authors

Hui Li

Dario Landa-Silva



Abstract

A multi-objective optimization problem can be solved by decomposing it into one or more single objective subproblems in some multi-objective metaheuristic algorithms. Each subproblem corresponds to one weighted aggregation function. For example, MOEA/D is an evolutionary multi-objective optimization (EMO) algorithm that attempts to optimize multiple subproblems simultaneously by evolving a population of solutions. However, the performance of MOEA/D highly depends on the initial setting and diversity of the weight vectors. In this paper, we present an improved version of MOEA/D, called EMOSA, which incorporates an advanced local search technique (simulated annealing) and adapts the search directions (weight vectors) corresponding to various subproblems. In EMOSA, the weight vector of each subproblem is adaptively modified at the lowest temperature in order to diversify the search towards the unexplored parts of the Pareto-optimal front. Our computational results show that EMOSA outperforms six other well-established multi-objective metaheuristic algorithms on both the (constrained)multi-objective knapsack problemand the (unconstrained) multi-objective traveling salesman problem. Moreover, the effects of the main algorithmic components and parameter sensitivities on the search performance of EMOSA are experimentally investigated.

Journal Article Type Article
Publication Date Jan 1, 2011
Journal Evolutionary Computation
Print ISSN 1063-6560
Electronic ISSN 1530-9304
Publisher Massachusetts Institute of Technology Press
Peer Reviewed Peer Reviewed
Volume 19
Issue 4
APA6 Citation Li, H., & Landa-Silva, D. (2011). An adaptive evolutionary multi-objective approach based on simulated annealing. Evolutionary Computation, 19(4), doi:10.1162/EVCO_a_00038
DOI https://doi.org/10.1162/EVCO_a_00038
Keywords multiobjective optimization, simulated annealing, local search, combinatorial optimization
Publisher URL http://www.mitpressjournals.org/doi/abs/10.1162/EVCO_a_00038#.Vv_I3vkrJpg
Copyright Statement Copyright information regarding this work can be found at the following address: http://eprints.nottingh.../end_user_agreement.pdf
Additional Information © 2011 by the Massachusetts Institute of Technology.

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Copyright Statement
Copyright information regarding this work can be found at the following address: http://eprints.nottingham.ac.uk/end_user_agreement.pdf





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