Particle Swarm Optimization: A Comprehensive Survey

Particle swarm optimization (PSO) is one of the most well-regarded swarm-based algorithms in the literature. Moreover, this paper reviews recent studies that utilize PSO to solve feature selection problems.

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• 1 Although the original PSO has shown good optimization performance, it still severely suffers from premature convergence.
• 2 As a result, many researchers have been modifying it resulting in a large number of PSO variants with either slightly or significantly better performance.
• 3 Mainly, the standard PSO has been modified by four main strategies: modification of the PSO controlling parameters, hybridizing PSO with other well-known meta-heuristic algorithms such as genetic algorithm (GA) and differential evolution (DE), cooperation and multi-swarm techniques.

This research advances how AI systems learn, reason, and solve problems — with direct implications for automation and scientific discovery.

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