An Overview of Variants and Advancements of PSO Algorithm
Source abstract
Abstract
Particle swarm optimization (PSO) is one of the most famous swarm-based optimization techniques inspired by nature. Due to its properties of flexibility and easy implementation, there is an enormous increase in the popularity of this nature-inspired technique. Particle swarm optimization (PSO) has gained prompt attention from every field of researchers. Since its origin in 1995 till now, researchers have improved the original Particle swarm optimization (PSO) in varying ways. They have derived new versions of it, such as the published theoretical studies on various parameters of PSO, proposed many variants of the algorithm and numerous other advances. In the present paper, an overview of the PSO algorithm is presented. On the one hand, the basic concepts and parameters of PSO are explained, on the other hand, various advances in relation to PSO, including its modifications, extensions, hybridization, theoretical analysis, are included.
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Research context
OpenAlex topics: Metaheuristic Optimization Algorithms Research · Advanced Algorithms and Applications · Machine Learning and ELM
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Mathematics, Punjabi University, Patiala 147002, Punjab, India
University College, Chunni Kalan, Dist. Fatehgarh Sahib, Patiala 140307, Punjab, India
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