Control Parameter Importance and Sensitivity Analysis of the Multi-Guide Particle Swarm Optimization Algorithm

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Abstract

The multi-guide particle swarm optimization (MGPSO) algorithm is a multi-objective optimization algorithm that uses multiple swarms, each swarm focusing on an individual objective. This paper conducts an importance and sensitivity analysis on the MGPSO control parameters using functional analysis of variance (fANOVA). The fANOVA process quantifies the control parameter importance through analysing variance in the objective function values associated with a change in control parameter values. The results indicate that the inertia component value has the greatest sensitivity and is the most important control parameter to tune when optimizing the MGPSO.

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Carolus, T. G., & Engelbrecht, A. P. (2020). Control Parameter Importance and Sensitivity Analysis of the Multi-Guide Particle Swarm Optimization Algorithm. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 12421 LNCS, pp. 96–106). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-60376-2_8

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