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Sigmoid Comprehensive Learning Particle Swarm Optimization
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DOI：10.3390/math14111854.png)
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Abstract
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As a particle swarm optimization (PSO) variant introduced in our earlier work, adaptive comprehensive learning PSO (ACLPSO) relies on two critical coefficients, i.e., the interval scaling coefficient s and the probability tradeoff coefficient v to regulate dimensional maximum velocities and per-dimension learning probabilities. ACLPSO prescribes four fixed value pairs for the two coefficients; however, no principled selection criterion exists, forcing practitioners to tune the pairs manually across different problems. This paper introduces sigmoid comprehensive learning PSO (SCLPSO), removing the need for such manual tuning by defining iterative assignment rules in which s and v evolve according to sigmoid schedules. SCLPSO also refines the tradeoff term in the per-dimension learning probability update equation to strengthen exploration capability. While prior work evaluated ACLPSO and competing PSO variants solely on sixteen classical benchmark functions, the present paper extends assessment to the congress on evolutionary computation (CEC) 2013 multimodal test suite to probe generalization more thoroughly. Experimental results demonstrate that SCLPSO identifies the global optimum or a near-optimal solution on the vast majority of test functions and consistently surpasses ACLPSO under any fixed coefficient pair.
Keywords:
particle swarm optimization
comprehensive learning
sigmoid function
CEC2013 multimodal test suite
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