An Improved Particle Swarm Optimization Algorithm for Global Multidimensional Optimization
| Autor: | Rkia Fajr, Abdelaziz Bouroumi |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2017 |
| Předmět: |
Computer science
Science collaborative learning 02 engineering and technology Swarm intelligence 90c59 Artificial Intelligence 0202 electrical engineering electronic engineering information engineering multidimensional functions Global optimization particle swarm optimization business.industry swarm intelligence global optimization Particle swarm optimization 020206 networking & telecommunications Collaborative learning QA75.5-76.95 Multidimensional optimization Electronic computers. Computer science 020201 artificial intelligence & image processing Artificial intelligence business Software Information Systems |
| Zdroj: | Journal of Intelligent Systems, Vol 29, Iss 1, Pp 127-142 (2017) |
| ISSN: | 0334-1860 |
| Popis: | This paper introduces a new variant of the particle swarm optimization (PSO) algorithm, designed for global optimization of multidimensional functions. The goal of this variant, called ImPSO, is to improve the exploration and exploitation abilities of the algorithm by introducing a new operation in the iterative search process. The use of this operation is governed by a stochastic rule that ensures either the exploration of new regions of the search space or the exploitation of good intermediate solutions. The proposed method is inspired by collaborative human learning and uses as a starting point a basic PSO variant with constriction factor and velocity clamping. Simulation results that show the ability of ImPSO to locate the global optima of multidimensional functions are presented for 10 well-know benchmark functions from CEC-2013 and CEC-2005. These results are compared with the PSO variant used as starting point, three other PSO variants, one of which is based on human learning strategies, and three alternative evolutionary computing methods. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|