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The Scientific World Journal
Volume 2014, Article ID 709738, 12 pages
http://dx.doi.org/10.1155/2014/709738
Research Article

A Novel Hybrid Self-Adaptive Bat Algorithm

1Faculty of Electrical Engineering and Computer Science, University of Maribor, Smetanova 17, 2000 Maribor, Slovenia
2Department of Computer and Information Science, University of Macau, Avenue Padre Tomas Pereira, Taipa, Macau

Received 17 November 2013; Accepted 9 March 2014; Published 9 April 2014

Academic Editors: J. Shu and F. Yu

Copyright © 2014 Iztok Fister Jr. et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Nature-inspired algorithms attract many researchers worldwide for solving the hardest optimization problems. One of the newest members of this extensive family is the bat algorithm. To date, many variants of this algorithm have emerged for solving continuous as well as combinatorial problems. One of the more promising variants, a self-adaptive bat algorithm, has recently been proposed that enables a self-adaptation of its control parameters. In this paper, we have hybridized this algorithm using different DE strategies and applied these as a local search heuristics for improving the current best solution directing the swarm of a solution towards the better regions within a search space. The results of exhaustive experiments were promising and have encouraged us to invest more efforts into developing in this direction.