Name
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. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1016/j.eswa.2011.06.031 | Expert Syst. Appl. |
Keywords | Field | DocType |
bacterial population,benchmark optimization problem,optimization algorithm,bacterial foraging optimization,convergence issue,population-based optimization technique,evolutionary optimization,foraging mechanism,numerical optimization,synergetic bacterial swarming optimization,bfo algorithm,bfo bacterium,particle swarm optimization algorithm,synergetic swarm,curse of dimensionality,optimization problem | Particle swarm optimization,Population,Mathematical optimization,Derivative-free optimization,Local optimum,Computer science,Meta-optimization,Multi-swarm optimization,Artificial intelligence,Optimization problem,Machine learning,Metaheuristic | Journal |
Volume | Issue | ISSN |
38 | 12 | Expert Systems With Applications |
Citations | PageRank | References |
6 | 0.45 | 15 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sotirios P. Chatzis | 1 | 250 | 24.25 |
| Spyros Koukas | 2 | 6 | 0.45 |