Expected binary particle swarm optimization

Document Type : Research Paper

Authors

1 Department of Computer Engineering, Lorestan University, Khorramabad, Iran

2 Department of Computer Engineering, Technical and Vocational University (TVU), Tehran, Iran

Abstract

Binary Particle Swarm Optimization (BPSO), proposed by Kennedy and Eberhart, extends PSO to binary search spaces. However, BPSO suffers from computational complexity due to velocity-based position updates and limited scalability in high-dimensional problems. In this paper, we introduce Expected BPSO (EBPSO), a simplified and faster variant that removes the velocity component and directly uses a probabilistic position update mechanism inspired by expected particle behavior. We theoretically analyze EBPSO’s convergence and evaluate its performance across two domains: (1) ten scalable binary benchmark functions (F1–F10) and (2) feature selection for classification using four real-world datasets (Breast Cancer, Iris, Wine, and Digits). EBPSO consistently outperforms BPSO, Binary GA, and other recent binary metaheuristics (e.g., BDO, BSCA, BGWO, BRKO) in both solution quality and runtime. For example, EBPSO achieved up to 15× speedup over BPSO and maintained a competitive advantage across all tested dimensions. In the feature selection task, EBPSO was used within a wrapper model using an SVM classifier. It reached accuracies of 99.07\% on Digits, 99.44\% on Wine, and 98.42\% on Breast Cancer datasets while selecting fewer features than other methods. Statistical significance was confirmed using paired t-tests and Wilcoxon signed-rank tests, both yielding p-values < 0.01 across all evaluations. Overall, EBPSO demonstrates superior performance, scalability, and statistical robustness, making it a promising tool for large-scale binary optimization and efficient feature selection.

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Main Subjects


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Articles in Press, Accepted Manuscript
Available Online from 09 August 2025
  • Receive Date: 19 January 2025
  • Revise Date: 15 July 2025
  • Accept Date: 09 August 2025