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Unconventional Methods for Particle Swarm Optimization II
This special issue belongs to the section “Multidisciplinary Applications“.
Special Issue Information
Dear Colleagues,
Particle swarm optimization (PSO) is a population-based optimization metaheuristic inspired by the collective dynamics of groups of animals, like insects, birds, and fishes. Recent research trends have indicated the potentiality of the approach and its possibilities for improvement. With the term “unconventional methods for PSO”, we mean modifications of the standard PSO algorithm with the objective of improving its performance or bestowing on it some particular properties. Examples include new methods for choosing the inertia weight, constriction factor, or cognition and social weights; parallelizing PSO in several different ways; defining hybrid algorithms in which PSO is integrated with other types of metaheuristic optimization methods; and entropy-based PSO. The study of unconventional methods for PSO is a very lively and active research field, and the objective of this Special Issue is to present a collection of studies in this recent and exciting area, with a particular focus on entropic, information-theoretic, or probability-theoretic techniques.
Prof. Dr. Leonardo Vanneschi
Guest Editor
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Keywords
- entropy-based PSO
- information theory for PSO
- probability theory for PSO
- theoretically motivated hybrid PSO systems
- theoretically motivated parallelizations of PSO
- theoretically motivated niching
- new acceleration strategies
- automatic, static, or dynamic parameter setting
- improvements and/or specializations of particle movements
- PSO for the optimization/improvement of machine learning methods
- real-life applications using theoretically motivated unconventional PSO systems
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Published Papers
Entropy - ISSN 1099-4300