Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients

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Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients (2004)

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by Asanga Ratnaweera , Saman K. Halgamuge , Harry C. Watson

Venue:	IEEE Transactions on Evolutionary Computation
Citations:	193 - 2 self

BibTeX

@ARTICLE{Ratnaweera04self-organizinghierarchical,
    author = {Asanga Ratnaweera and Saman K. Halgamuge and Harry C. Watson},
    title = {Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients},
    journal = {IEEE Transactions on Evolutionary Computation},
    year = {2004},
    pages = {240--255}
}

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Abstract

Abstract—This paper introduces a novel parameter automation strategy for the particle swarm algorithm and two further extensions to improve its performance after a predefined number of generations. Initially, to efficiently control the local search and convergence to the global optimum solution, time-varying acceleration coefficients (TVAC) are introduced in addition to the time-varying inertia weight factor in particle swarm optimization (PSO). From the basis of TVAC, two new strategies are discussed to improve the performance of the PSO. First, the concept of “mutation ” is introduced to the particle swarm optimization along with TVAC (MPSO-TVAC), by adding a small perturbation to a randomly selected modulus of the velocity vector of a random particle by predefined probability. Second, we introduce a novel particle swarm concept “self-organizing hierarchical particle swarm optimizer with TVAC (HPSO-TVAC). ” Under this method, only the “social ” part and the “cognitive ” part of the particle swarm strategy are considered to estimate the new velocity of each particle and particles are reinitialized whenever they are stagnated in the search space. In addition, to overcome the difficulties of selecting an appropriate mutation step size for different problems, a time-varying mutation step size was introduced. Further, for most of the benchmarks, mutation probability is found to be insensitive to the performance of MPSO-TVAC method. On the other hand, the effect of reinitialization velocity on the performance of HPSO-TVAC method is also observed. Time-varying reinitialization step size is found to be an efficient parameter optimization strategy for HPSO-TVAC method. The HPSO-TVAC strategy outperformed all the methods considered in this investigation for most of the functions. Furthermore, it has also been observed that both the MPSO and HPSO strategies perform poorly when the acceleration coefficients are fixed at two. Index Terms—Acceleration coefficients, hierarchical particle swarm, mutation, particle swarm, reinitialization. I.

Keyphrases

self-organizing hierarchical particle swarm optimizer time-varying acceleration coefficient particle swarm optimization hpso-tvac method mutation probability global optimum solution social part particle swarm algorithm acceleration coefficient particle swarm reinitialization velocity efficient parameter optimization strategy new strategy small perturbation local search particle swarm strategy search space new velocity time-varying inertia weight factor hpso strategy novel particle swarm hierarchical particle swarm different problem predefined probability index term acceleration coefficient appropriate mutation step size time-varying mutation step size hpso-tvac strategy predefined number velocity vector mpso-tvac method novel parameter automation strategy time-varying reinitialization step size random particle cognitive part

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