Why sigmoid function is used in binary particle swarm optimization?
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Abstract
This paper presents a modified binary particle swarm optimization (BPSO) which adopts concepts of the genotype–phenotype representation and the mutation operator of genetic algorithms. Its main feature is that the BPSO can be treated as a continuous PSO. The proposed BPSO algorithm is tested on various benchmark functions, and its performance is compared with that of the original BPSO. Experimental results show that the modified BPSO outperforms the original BPSO algorithm.
This paper presents a modified binary particle swarm optimization (BPSO) which adopts concepts of the genotype–phenotype representation and the mutation operator of genetic algorithms. Its main feature is that the BPSO can be treated as a continuous PSO. The proposed BPSO algorithm is tested on various benchmark functions, and its performance is compared with that of the original BPSO. Experimental results show that the modified BPSO outperforms the original BPSO algorithm.
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