Open AccessJournal Article
A Parallel Particle Swarm Optimization Algorithm with Communication Strategies
TLDR
A parallel version of the particle swarm optimization (PPSO) algorithm together with three communication strategies which can be used according to the independence of the data, which demonstrates the usefulness of the proposed PPSO algorithm.Abstract:
Particle swarm optimization (PSO) is an alternative population-based evolutionary computation technique. It has been shown to be capable of optimizing hard mathematical problems in continuous or binary space. We present here a parallel version of the particle swarm optimization (PPSO) algorithm together with three communication strategies which can be used according to the independence of the data. The first strategy is designed for solution parameters that are independent or are only loosely correlated, such as the Rosenbrock and Rastrigrin functions. The second communication strategy can be applied to parameters that are more strongly correlated such as the Griewank function. In cases where the properties of the parameters are unknown, a third hybrid communication strategy can be used. Experimental results demonstrate the usefulness of the proposed PPSO algorithm.read more
Citations
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Multiple Particle Swarm Optimizers with Inertia Weight for Multi-objective Optimization
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References
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Proceedings ArticleDOI
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Proceedings ArticleDOI
A modified particle swarm optimizer
Yuhui Shi,Russell C. Eberhart +1 more
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Journal ArticleDOI
The particle swarm - explosion, stability, and convergence in a multidimensional complex space
M. Clerc,James Kennedy +1 more
TL;DR: This paper analyzes a particle's trajectory as it moves in discrete time, then progresses to the view of it in continuous time, leading to a generalized model of the algorithm, containing a set of coefficients to control the system's convergence tendencies.