Publication: Position Update And Search Strategy Based Binary Particle Swarm Optimization For Feature Selection
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Date
2025-09
Authors
Sani, Tijjani
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Abstract
The rapid expansion of data due to the advances in science and technology causes an increase in both the number of instances and features (dimensions). When the dimensionality of data increases, the computational cost also increases, mainly exponentially. Consequently, designing a feature selection method that can select relevant features is very important. The binary particle swarm optimization (bpso) algorithm often converges to a local optimum quickly (local optima stagnation), missing better opportunities that lead to premature convergence. In addition, the original position update formula cannot effectively balance exploitation (local search) and exploration (global search) in the search process. Hence, this research work proposes a modified binary particle swarm optimization method for feature selection. Its main feature first, is the application of a computational reduction which lowers the number of redundant features and computation costs. Secondly, determines the probability of the particles switching positions using position values rather than velocity values which speed up convergence and overcome the premature convergence simultaneously. Two variant methods for determining the probability of changing the position of a particle element were introduced. These result in the two variants of enhanced binary particle swarm optimization (ebpso) for feature selection, called ebpso1 and ebpso2
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Position Update Search Strategy Based Binary