Welcome to the InfoSci Platform
Reference Hub7
A New Differential Evolution Based Metaheuristic for Discrete Optimization

A New Differential Evolution Based Metaheuristic for Discrete Optimization

Ricardo Sérgio Prado, Rodrigo César Pedrosa Silva, Frederico Gadelha Guimarães, Oriane Magela Neto
Copyright: © 2010 |Volume: 1 |Issue: 2 |Pages: 18
ISSN: 1947-928X|EISSN: 1947-9298|EISBN13: 9781609604516|DOI: 10.4018/jncr.2010040102
Cite Article Cite Article

MLA

Prado, Ricardo Sérgio, et al. "A New Differential Evolution Based Metaheuristic for Discrete Optimization." IJNCR vol.1, no.2 2010: pp.15-32. http://doi.org/10.4018/jncr.2010040102

APA

Prado, R. S., Silva, R. C., Guimarães, F. G., & Neto, O. M. (2010). A New Differential Evolution Based Metaheuristic for Discrete Optimization. International Journal of Natural Computing Research (IJNCR), 1(2), 15-32. http://doi.org/10.4018/jncr.2010040102

Chicago

Prado, Ricardo Sérgio, et al. "A New Differential Evolution Based Metaheuristic for Discrete Optimization," International Journal of Natural Computing Research (IJNCR) 1, no.2: 15-32. http://doi.org/10.4018/jncr.2010040102

Export Reference

Mendeley
Favorite Full-Issue Download

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.

Request Access

You do not own this content. Please login to recommend this title to your institution's librarian or purchase it from the IGI Global bookstore.