Please use this identifier to cite or link to this item: http://ktisis.cut.ac.cy/handle/10488/7223
Title: Numerical optimization using synergetic swarms of foraging bacterial populations
Authors: Chatzis, Sotirios P. 
Koukas, Spyros 
Chatzis, Sotirios P. 
Koukas, Spyros 
Keywords: Computer science
Artificial intelligence
Expert systems (Computer science)
Algorithms
Bacteria
Population--Statistics
Bacteriology
Issue Date: 2011
Publisher: Elsevier
Source: Expert systems with applications, 2011, Volume 38, Issue 12, Pages 15332–15343
Abstract: The bacterial foraging optimization (BFO) algorithm is a popular stochastic, population-based optimization technique that can be applied to a wide range of problems. Two are the major issues the BFO algorithm is confronted with: first, the foraging mechanism of BFO might in some cases induce the attraction of bacteria gathered near the global optimum by bacteria gathered to local optima, thus slowing down the whole population convergence. Second, BFO is susceptible to the curse-of-dimensionality, which makes it significantly harder to find the global optimum of a high-dimensional problem, compared to a low-dimensional problem with similar topology. In this paper, we introduce a novel BFO-based optimization algorithm aiming to address these issues, the synergetic bacterial swarming optimization (SBSO) algorithm. Our novel approach consists of: (i) the introduction of the swarming dynamics of the particle swarm optimization algorithm in the context of BFO, in order to ameliorate the convergence issues of the BFO bacteria foraging mechanism; and (ii) the utilization of multiple populations to optimize different components of the solution vector cooperatively, so as to mitigate the curse-of-dimensionality issues of the algorithm. We demonstrate the efficacy of our approach on several benchmark optimization problems
URI: http://ktisis.cut.ac.cy/handle/10488/7223
ISSN: 0957-4174
DOI: 10.1016/j.eswa.2011.06.031
Rights: © 2011 Elsevier Ltd. All rights reserved
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