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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Neural fuzzy forecasting of the china yuan to US dollar exchange rate: a swarm intelligence approach
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Parallel implementations of the Complex-RF algorithm
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TL;DR: Three methods for parallelizing the Complex-RF algorithm have been implemented and evaluated and the suggested methods outperform both a particle swarm and a differential evolution algorithm with up to 32 threads.
Review on Swarm Intelligence for Optimization
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TL;DR: The concept of four swarm intelligence methods, including Bat Algorithm (BA), Evolved Bat Al algorithm (EBA), Cat Swarm Optimization (CSO), and Parallel Cat Swarmoptimization (PCSO) are given in a comprehensive way are given.
References
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Proceedings ArticleDOI
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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.