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Particle swarm optimization: A study of particle displacement for solving continuous and combinatorial optimization problems

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  • Kemmoé Tchomté, Sylverin
  • Gourgand, Michel

Abstract

This article addresses the particle swarm optimization (PSO) method. It is a recent proposed algorithm by Kennedy and Eberhart [1995. Particle swarm optimization. In: Proceedings of the IEEE International Conference on Neural Networks (Perth, Australia), vol. IV, IEEE Service Center, Piscataway, NJ, pp. 1942-1948]. This optimization method is motivated by social behaviour of organisms such as bird flocking and fish schooling. PSO algorithm is not only a tool for optimization, but also a tool for representing socio-cognition of human and artificial agents, based on principles of social behaviour. Some scientists suggest that knowledge is optimized by social interaction and thinking is not only private but also interpersonal. PSO as an optimization tool, provides a population-based search procedure in which individuals called particles change their position (state) with time. In a PSO system, particles fly in a multidimensional search space. During flight, each particle adjusts its position according to its own experience, and according to the experience of neighbours, making use of the best position encountered by itself and its neighbours. In this paper, we propose firstly, an extension of the PSO system that integrates a new displacement of the particles (the balance between the intensification process and the diversification process) and we highlight a relation between the coefficients of update of each dimension velocity between the classical PSO algorithm and the extension. Secondly, we propose an adaptation of this extension of PSO algorithm to solve combinatorial optimization problem with precedence constraints in general and resource-constrained project scheduling problem in particular. The numerical experiments are done on the main continuous functions and on the resource-constrained project scheduling problem (RCPSP) instances provided by the psplib. The results obtained are encouraging and push us into accepting than both PSO algorithm and extensions proposed based on the new particles displacement are a promising direction for research.

Suggested Citation

  • Kemmoé Tchomté, Sylverin & Gourgand, Michel, 2009. "Particle swarm optimization: A study of particle displacement for solving continuous and combinatorial optimization problems," International Journal of Production Economics, Elsevier, vol. 121(1), pages 57-67, September.
  • Handle: RePEc:eee:proeco:v:121:y:2009:i:1:p:57-67
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    References listed on IDEAS

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    2. Dongmin Wang & Xiangnan Liu, 2018. "Comparative Analysis of GF-1 and HJ-1 Data to Derive the Optimal Scale for Monitoring Heavy Metal Stress in Rice," IJERPH, MDPI, vol. 15(3), pages 1-15, March.
    3. Albert Corominas & Alberto García-Villoria & Rafael Pastor, 2013. "Metaheuristic algorithms hybridised with variable neighbourhood search for solving the response time variability problem," TOP: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 21(2), pages 296-312, July.
    4. Nearchou, Andreas C., 2011. "Maximizing production rate and workload smoothing in assembly lines using particle swarm optimization," International Journal of Production Economics, Elsevier, vol. 129(2), pages 242-250, February.
    5. Mirko M. Stojiljković & Mladen M. Stojiljković & Bratislav D. Blagojević, 2014. "Multi-Objective Combinatorial Optimization of Trigeneration Plants Based on Metaheuristics," Energies, MDPI, vol. 7(12), pages 1-28, December.

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