Particle swarm optimization with mutation

Stacey, A.; Jancic, M.; Grundy, Ian H.

doi:10.1109/cec.2003.1299838

Cited by 183 publications

(82 citation statements)

References 8 publications

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“…Some related researches about its improvement and applications has also been proposed (Shi and Eberhart, 1998;Gaing, 2003;Stacey et al, 2003;Qin et al, 2004). It maintains several particles (each represents a solution) and simulates the behavior of bird flocking to find the final solutions.…”

Section: Review Of Particle Swarm Optimizationmentioning

confidence: 99%

Solving the <i>K-of-N</i> Lifetime Problem by PSO

Hong

Shiu²

2010

Int. J. Eng. Sci. Tech

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In wireless sensor networks, application nodes (ANs) may be produced by different manufacturers and may own different data transmission rates, initial energies and parameter values. When different kinds of ANs exist in a wireless network, it is hard to find the optimal base-station (BS) locations for minimizing power consumption and prolonging network lifetime. In this paper, a heuristic algorithm based on PSO is thus proposed to solve the K-of-N lifetime problem under general power-consumption constraints in wireless sensor networks. Give N ANs, the network survives as long as there are at least K ANs alive. The proposed approach can search for nearly optimal BS locations in heterogeneous sensor networks, where application nodes may own different data transmission rates, initial energies and parameter values. Experimental results also show the good performance of the proposed PSO approach and the effects of the parameters on the results. The proposed algorithm can thus help maximize the K-of-N network lifetime in wireless sensor networks.

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Section: Review Of Particle Swarm Optimizationmentioning

confidence: 99%

Solving the <i>K-of-N</i> Lifetime Problem by PSO

Hong

Shiu²

2010

Int. J. Eng. Sci. Tech

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“…However, the use of other probability distributions may improve the ability to fine-tune or even escape from local optima. In the meantime, it has been proposed that the Gaussian and/or Cauchy probability distributions be used to generate random numbers to update the velocity equation (Miranda and Fonseca, 2002;Wei et al, 2002, Secrest et al, 2003Stacey et al, 2003;Esquivel and Coello, 2003;Higashi and Iba, 2003;Coelho and Krohling, 2005;Ting et al, 2006;Coelho, 2006, Coelho andAlotto, 2008;Coelho and Lee, 2008).…”

Section: Particle Swarm Optimization Methods (Pso)mentioning

confidence: 99%

Alternative fuels mixture in cement industry kilns employing Particle Swarm Optimization algorithm

Carpio¹,

Júnior

Coelho³

et al. 2008

J. Braz. Soc. Mech. Sci. & Eng.

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“…Hybrid PSO algorithms have been developed that incorporate operators from evolutionary algorithms into PSO. Many PSO hybrids with evolutionary algorithms make use of some form of mutation, applied to either velocity vectors or position vectors (Miranda and Fonseca, 2002;van den Bergh and Engelbrecht, 2002;Wei et al, 2002;Higashi and Iba, 2003;Stacey et al, 2003). Only a few studies can be found where crossover operators were used within PSO algorithms.…”

Section: Particle Swarm Optimization With Crossovermentioning

confidence: 99%

Particle swarm optimization with crossover: a review and empirical analysis

Engelbrecht

2015

Artif Intell Rev

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Since its inception in 1995, many improvements to the original particle swarm optimization (PSO) algorithm have been developed. This paper reviews one class of such PSO variations, i.e. PSO algorithms that make use of crossover operators. The review is supplemented with a more extensive sensitivity analysis of the crossover PSO algorithms than provided in the original publications. Two adaptations of a parent-centric crossover PSO algorithm are provided, resulting in improvements with respect to solution accuracy compared to the original parentcentric PSO algorithms. The paper then provides an extensive empirical analysis on a large benchmark of minimization problems, with the objective to identify those crossover PSO algorithms that perform best with respect to accuracy, success rate, and efficiency.

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Particle swarm optimization with mutation

Cited by 183 publications

References 8 publications

Solving the <i>K-of-N</i> Lifetime Problem by PSO

Solving the <i>K-of-N</i> Lifetime Problem by PSO

Alternative fuels mixture in cement industry kilns employing Particle Swarm Optimization algorithm

Particle swarm optimization with crossover: a review and empirical analysis

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