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Numerical optimization using synergetic swarms of foraging bacterial populations
- Source :
- Expert Systems with Applications.
- Publication Year :
- 2011
- Publisher :
- Elsevier BV, 2011.
-
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.
- Subjects :
- Artificial intelligence
Computer and Information Sciences
Mathematical optimization
Expert systems (Computer science)
Optimization problem
Meta-optimization
Computer science
Foraging
Population
Swarming (honey bee)
Local optimum
Artificial Intelligence
Derivative-free optimization
Multi-swarm optimization
education
Metaheuristic
education.field_of_study
Bacteria
General Engineering
Population--Statistics
Particle swarm optimization
Bacteriology
Computer Science Applications
Global optimum
Engineering and Technology
Algorithms
Subjects
Details
- ISSN :
- 09574174
- Database :
- OpenAIRE
- Journal :
- Expert Systems with Applications
- Accession number :
- edsair.doi.dedup.....a8a782c50f54fc7f60f412ce3b724f1e