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Particle swarm optimizer with adaptive tabu and mutation: A unified framework for efficient mutation operators

Published:25 February 2010Publication History
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Abstract

Evolutionary Algorithms (EAs) and Swarm Intelligence (SI) are widely used to tackle black-box global optimization problems when no prior knowledge is available. In order to increase search diversity and avoid stagnation in local optima, the mutation operator was introduced and has been extensively studied in EAs and SI-based algorithms. However, the performance after introducing mutation can be affected in many aspects and the parameters used to perform mutations are very hard to determine. For the purpose of developing efficient mutation operators, this article proposes a unified tabu and mutation framework with parameter adaptations in the context of the Particle Swarm Optimizer (PSO). The proposed framework is a significant extension of our preliminary work [Wang et al. 2007]. Empirical studies on 25 benchmark functions indicate that under the proposed framework: (1) excellent performance can be achieved even with a small number of mutations; (2) the derived algorithm consistently performs well on diverse types of problems and overall performance even surpasses the state-of-the-art PSO variants and representative mutation-based EAs; and (3) fast convergence rates can be preserved despite the use of a long jump mutation operator (the Cauchy mutation).

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  1. Particle swarm optimizer with adaptive tabu and mutation: A unified framework for efficient mutation operators

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        Giuseppina Carla Gini

        Particle swarm optimization (PSO) is an active research topic in the area of optimization algorithms. This paper, which integrates a tabu search method in the PSO, establishes three general principles-the when, what, and why-for performing mutations, in order to escape from local minima. The resulting algorithm, adaptive tabu and mutation (ATM)-PSO, guides the mutation of the particles. When the personal fitness of a particle is stable in the next generations, the geometric area around the particle is considered a tabu area; the particles inside it are mutated if frequently tabued. The results of the algorithm are compared with many other variants of PSO on a large set of benchmark problems. ATM-PSO consistently gives good or better performance. There are very few cases when ATM-PSO underperforms other methods. The good points of this algorithm are that it improves results, while the number of mutations is contained, and the computational complexity is acceptable. The drawback of the method is the high number of parameters to set. This is not a big issue, since a standard parameterization on all problems always gives good performance. The one possible concern is the scalability of the method to real-world problems, where the number of variables can be in the hundreds and the degree of linkage is very high. Online Computing Reviews Service

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