An adaptive evolutionary multi-objective approach based on simulated annealing

Li, Hui and Landa-Silva, Dario (2011) An adaptive evolutionary multi-objective approach based on simulated annealing. Evolutionary Computation, 19 (4). pp. 561-595. ISSN 1530-9304

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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.

Item Type: Article
Additional Information: © 2011 by the Massachusetts Institute of Technology.
Keywords: multiobjective optimization, simulated annealing, local search, combinatorial optimization
Schools/Departments: University of Nottingham, UK > Faculty of Science > School of Computer Science
Identification Number:
Depositing User: Landa-Silva, Dario
Date Deposited: 04 Apr 2016 08:17
Last Modified: 04 May 2020 20:23

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