Numerical optimization using synergetic swarms of foraging bacterial populations

Chatzis, SP; Koukas, S

dc.contributor.author	Chatzis, SP	en
dc.contributor.author	Koukas, S	en
dc.date.accessioned	2014-03-01T01:36:32Z
dc.date.available	2014-03-01T01:36:32Z
dc.date.issued	2011	en
dc.identifier.issn	0957-4174	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/21323
dc.subject	Evolutionary optimization	en
dc.subject.classification	Computer Science, Artificial Intelligence	en
dc.subject.classification	Engineering, Electrical & Electronic	en
dc.subject.classification	Operations Research & Management Science	en
dc.subject.other	Bacterial foraging optimization	en
dc.subject.other	Bacterial population	en
dc.subject.other	Evolutionary optimizations	en
dc.subject.other	Global optimum	en
dc.subject.other	High-dimensional problems	en
dc.subject.other	Local optima	en
dc.subject.other	Numerical optimizations	en
dc.subject.other	Optimization algorithms	en
dc.subject.other	Optimization problems	en
dc.subject.other	Particle swarm optimization algorithm	en
dc.subject.other	Population convergence	en
dc.subject.other	Population-based optimization	en
dc.subject.other	Solution vectors	en
dc.subject.other	Algorithms	en
dc.subject.other	Bacteria	en
dc.subject.other	Bacteriology	en
dc.subject.other	Elastic moduli	en
dc.subject.other	Particle swarm optimization (PSO)	en
dc.subject.other	Population statistics	en
dc.subject.other	Convergence of numerical methods	en
dc.title	Numerical optimization using synergetic swarms of foraging bacterial populations	en
heal.type	journalArticle	en
heal.identifier.primary	10.1016/j.eswa.2011.06.031	en
heal.identifier.secondary	http://dx.doi.org/10.1016/j.eswa.2011.06.031	en
heal.language	English	en
heal.publicationDate	2011	en
heal.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. (C) 2011 Elsevier Ltd. All rights reserved.	en
heal.publisher	PERGAMON-ELSEVIER SCIENCE LTD	en
heal.journalName	Expert Systems with Applications	en
dc.identifier.doi	10.1016/j.eswa.2011.06.031	en
dc.identifier.isi	ISI:000295193400103	en
dc.identifier.volume	38	en
dc.identifier.issue	12	en
dc.identifier.spage	15332	en
dc.identifier.epage	15343	en