ArticleComputational intelligence and neuroscience2020
Z-Shaped Transfer Functions for Binary Particle Swarm Optimization Algorithm.
Article in Computational intelligence and neuroscience, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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8 citing papers in PubMed.
- Binary Particle Swarm Optimization Intelligent Feature Optimization Algorithm-Based Magnetic Resonance Image in the Diagnosis of Adrenal Tumor.Contrast media & molecular imaging · 2022Trial
- New Binary Reptile Search Algorithms for Binary Optimization Problems.Biomimetics (Basel, Switzerland) · 2025Article
- A Transfer Function-Based Binary Version of Improved Pied Kingfisher Optimizer for Solving the Uncapacitated Facility Location Problem.Biomimetics (Basel, Switzerland) · 2025Article
- Integrating EPSOSA-BP neural network algorithm for enhanced accuracy and robustness in optimizing coronary artery disease prediction.Scientific reports · 2024Article
- Challenging the Limits of Binarization: A New Scheme Selection Policy Using Reinforcement Learning Techniques for Binary Combinatorial Problem Solving.Biomimetics (Basel, Switzerland) · 2024Article
- Binarization of Metaheuristics: Is the Transfer Function Really Important?Biomimetics (Basel, Switzerland) · 2023Article
- Binary Restructuring Particle Swarm Optimization and Its Application.Biomimetics (Basel, Switzerland) · 2023Article
- A particle swarm optimization improved BP neural network intelligent model for electrocardiogram classification.BMC medical informatics and decision making · 2021Article
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3 authors.
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Abstract
Particle swarm optimization (PSO) algorithm is a swarm intelligent searching algorithm based on population that simulates the social behavior of birds, bees, or fish groups. The discrete binary particle swarm optimization (BPSO) algorithm maps the continuous search space to a binary space through a new transfer function, and the update process is designed to switch the position of the particles between 0 and 1 in the binary search space. Aiming at the existed BPSO algorithms which are easy to fall into the local optimum, a new Z-shaped probability transfer function is proposed to map the continuous search space to a binary space. By adopting nine typical benchmark functions, the proposed Z-probability transfer function and the V-shaped and S-shaped transfer functions are used to carry out the performance simulation experiments. The results show that the proposed Z-shaped probability transfer function improves the convergence speed and optimization accuracy of the BPSO algorithm.
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