Brits, REngelbrecht, APVan den Bergh, F2007-06-292007-06-292007Brits, R, Engelbrecht, AP and Van den Bergh. 2007. Locating multiple optima using particle swarm optimization. Applied Mathematics and Computation. Vol. 189, pp 1859-18830096-3003http://hdl.handle.net/10204/801http://www.sciencedirect.com/science/journal/00963003Many scientific and engineering applications require optimization methods to find more than one solution to multimodal optimization problems. This paper presents a new particle swarm optimization (PSO) technique to locate and refine multiple solutions to such problems. The technique, NichePSO, extends the inherent unimodal nature of the standard PSO approach by growing multiple swarms from an initial particle population. Each subswarm represents a different solution or niche; optimized individually. The outcome of the NichePSO algorithm is a set of particle swarms, each representing a unique solution. Experimental results are provided to show that NichePSO can successfully locate all optima on a small set of test functions. These results are compared with another PSO niching algorithm, lbest PSO, and two genetic algorithm niching approaches. The influence of control parameters is investigated, including the relationship between the swarm size and the number of solutions (niches). An initial scalability study is also done.enParticle swarm optimizationMulti-modal optimization problemsArticleBrits, R., Engelbrecht, A., & Van den Bergh, F. (2007). Locating multiple optima using particle swarm optimization. http://hdl.handle.net/10204/801Brits, R, AP Engelbrecht, and F Van den Bergh "Locating multiple optima using particle swarm optimization." (2007) http://hdl.handle.net/10204/801Brits R, Engelbrecht A, Van den Bergh F. Locating multiple optima using particle swarm optimization. 2007; http://hdl.handle.net/10204/801.TY - Article AU - Brits, R AU - Engelbrecht, AP AU - Van den Bergh, F AB - Many scientific and engineering applications require optimization methods to find more than one solution to multimodal optimization problems. This paper presents a new particle swarm optimization (PSO) technique to locate and refine multiple solutions to such problems. The technique, NichePSO, extends the inherent unimodal nature of the standard PSO approach by growing multiple swarms from an initial particle population. Each subswarm represents a different solution or niche; optimized individually. The outcome of the NichePSO algorithm is a set of particle swarms, each representing a unique solution. Experimental results are provided to show that NichePSO can successfully locate all optima on a small set of test functions. These results are compared with another PSO niching algorithm, lbest PSO, and two genetic algorithm niching approaches. The influence of control parameters is investigated, including the relationship between the swarm size and the number of solutions (niches). An initial scalability study is also done. DA - 2007 DB - ResearchSpace DP - CSIR KW - Particle swarm optimization KW - Multi-modal optimization problems LK - https://researchspace.csir.co.za PY - 2007 SM - 0096-3003 T1 - Locating multiple optima using particle swarm optimization TI - Locating multiple optima using particle swarm optimization UR - http://hdl.handle.net/10204/801 ER -