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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Journal ArticleDOI
A speculative approach to parallelization in particle swarm optimization
TL;DR: A speculative approach to the parallelization of PSO that is a new method of parallelization and not a new PSO algorithm or variant, and can relax the requirement of exactly reproducing PSO in an attempt to produce better results.
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An Improved Particle Swarm Optimization with Feasibility-Based Rules for Constrained Optimization Problems
TL;DR: This paper presents an improved particle swarm optimization (IPSO) to solve constrained optimization problems, which handles constraints based on certain feasibility-based rules, and a turbulence operator is incorporated into IPSO algorithm to overcome the premature convergence.
Book ChapterDOI
A New Framework for Optimization Based-On Hybrid Swarm Intelligence
TL;DR: A hybrid optimization algorithm based on Cat Swarm Optimization (CSO) and Artificial Bee Colony (ABC) and the hybrid framework combining different algorithms called Hybrid PCSOABC is presented.
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The Structure Optimization of Main Beam for Bridge Crane Based on An Improved PSO
TL;DR: The comparison results with the enumeration algorithm illustrated that MPSO can get best optimal solutions in much less calculation numbers.
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Parallelized Bat Algorithm with a Communication Strategy
TL;DR: A communication strategy for the parallelized Bat Algorithm optimization is proposed for solving numerical optimization problems and increases the accuracy of the BA on finding the near best solution.
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Yuhui Shi,Russell C. Eberhart +1 more
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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.