Generalized particle swarm optimization algorithm - Theoretical and empirical analysis with application in fault detection |
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Authors: | ?eljko Kanovi? Milan R Rapai?Zoran D Jeli?i? |
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Institution: | Faculty of Technical Sciences, Trg Dositeja Obradovi?a 6, 21000 Novi Sad, Serbia |
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Abstract: | A generalization of the particle swarm optimization (PSO) algorithm is presented in this paper. The novel optimizer, the Generalized PSO (GPSO), is inspired by linear control theory. It enables direct control over the key aspects of particle dynamics during the optimization process. A detailed theoretical and empirical analysis is presented, and parameter-tuning schemes are proposed. GPSO is compared to the classical PSO and genetic algorithm (GA) on a set of benchmark problems. The results clearly demonstrate the effectiveness of the proposed algorithm. Finally, an application of the GPSO algorithm to the fine-tuning of the support vector machines classifier for electrical machines fault detection is presented. |
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Keywords: | Analysis of algorithms Global optimization Particle swarm optimization Control theory Fault detection |
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