Abstract
The particle swarm optimization (PSO) algorithm has been the object of many studies and modifications for more than 25 years. Ranging from small refinements to the incorporation of sophisticated novel ideas, the majority of modifications proposed to this algorithm have been the result of a manual process in which developers try new designs based on their own knowledge and expertise. However, manually introducing changes is very time consuming and makes the systematic exploration of all the possible algorithm configurations a difficult process. In this article, we propose to use automatic design to overcome the limitations of having to manually find performing PSO algorithms. We develop a flexible software framework for PSO, called PSO-X, which is specifically designed to integrate the use of automatic configuration tools into the process of generating PSO algorithms. Our framework embodies a large number of algorithm components developed over more than 25 years of research that have allowed PSO to deal with a large variety of problems, and uses <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">irace</monospace> , a state-of-the-art configuration tool, to automatize the task of selecting and configuring PSO algorithms starting from these components. We show that <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">irace</monospace> is capable of finding high-performing instances of PSO algorithms never proposed before.
Highlights
C OMPUTATIONAL intelligence algorithms, such as particle swarm optimization (PSO) and evolutionary algorithms (EAs), are widely used to tackle complex optimization problems for which exact approaches are often impractical [1], [2]
We have categorized these algorithm components into five different groups: (1) those used to set the value of the main algorithm parameters, (2) those that control the distribution of particles positions in the search space, (3) those used to apply perturbation to the velocity and/or position vectors, (4) those regarding the construction and application of the random matrices, and (5) those related to the topology, model of influence and population size
We have proposed PSO-X, a flexible, automatically configurable framework that combines algorithm components and automatic configuration tools to create high performing PSO implementations
Summary
C OMPUTATIONAL intelligence algorithms, such as particle swarm optimization (PSO) and evolutionary algorithms (EAs), are widely used to tackle complex optimization problems for which exact approaches are often impractical [1], [2]. In [8], only the social and cognitive components of the velocity update rule can be automatically designed; in [9], the list of components includes the topology and swarm size, but the grammar that defines the rules to combine components is based on the standard version of PSO and makes difficult to include recent algorithm components To overcome these limitation, in this paper, we propose PSO-X , a flexible, component-based framework containing a large number of algorithm components previously proposed in the PSO literature.
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