Shelokar, P. S. ; Siarry, Patrick ; Jayaraman, V. K. ; Kulkarni, B. D. (2007) Particle swarm and ant colony algorithms hybridized for improved continuous optimization Applied Mathematics and Computation, 188 (1). pp. 129-142. ISSN 0096-3003
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Official URL: http://linkinghub.elsevier.com/retrieve/pii/S00963...
Related URL: http://dx.doi.org/10.1016/j.amc.2006.09.098
Abstract
This paper proposes PSACO (particle swarm ant colony optimization) algorithm for highly non-convex optimization problems. Both particle swarm optimization (PSO) and ant colony optimization (ACO) are co-operative, population-based global search swarm intelligence metaheuristics. PSO is inspired by social behavior of bird flocking or fish schooling, while ACO imitates foraging behavior of real life ants. In this study, we explore a simple pheromone-guided mechanism to improve the performance of PSO method for optimization of multimodal continuous functions. The proposed PSACO algorithm is tested on several benchmark functions from the usual literature. Numerical results comparisons with different metaheuristics demonstrate the effectiveness and efficiency of the proposed PSACO method.
| Item Type: | Article |
|---|---|
| Source: | Copyright of this article belongs to Elsevier Science. |
| Keywords: | Particle Swarm Optimization; Ant Colony; Metaheuristics; Global Optimization; Multimodal Continuous Functions |
| ID Code: | 17161 |
| Deposited On: | 16 Nov 2010 08:18 |
| Last Modified: | 06 Jun 2011 08:39 |
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