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A New Differential Evolution Based Metaheuristic for Discrete Optimization
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Author(s): Ricardo Sérgio Prado (Instituto Federal de Minas Gerais, Brazil), Rodrigo César Pedrosa Silva (Universidade Federal de Ouro Preto, Brazil), Frederico Gadelha Guimarães (Universidade Federal de Ouro Preto, Brazil)and Oriane M. Neto (Universidade Federal de Minas Gerais, Brazil)
Copyright: 2012
Pages: 16
Source title:
Nature-Inspired Computing Design, Development, and Applications
Source Author(s)/Editor(s): Leandro Nunes de Castro (Mackenzie University, Brazil)
DOI: 10.4018/978-1-4666-1574-8.ch006
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Abstract
The Differential Evolution (DE) algorithm is an important and powerful evolutionary optimizer in the context of continuous numerical optimization. Recently, some authors have proposed adaptations of its differential mutation mechanism to deal with combinatorial optimization, in particular permutation-based integer combinatorial problems. In this paper, the authors propose a novel and general DE-based metaheuristic that preserves its interesting search mechanism for discrete domains by defining the difference between two candidate solutions as a list of movements in the search space. In this way, the authors produce a more meaningful and general differential mutation for the context of combinatorial optimization problems. The movements in the list can then be applied to other candidate solutions in the population as required by the differential mutation operator. This paper presents results on instances of the Travelling Salesman Problem (TSP) and the N-Queen Problem (NQP) that suggest the adequacy of the proposed approach for adapting the differential mutation to discrete optimization.
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