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Neural Computing and Applications

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29.10.2015 | Original Article

Multi-objective particle swarm-differential evolution algorithm

verfasst von: Yi-xin Su, Rui Chi

Erschienen in: Neural Computing and Applications | Ausgabe 2/2017

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Abstract

A multi-objective particle swarm-differential evolution algorithm (MOPSDE) is proposed that combined a particle swarm optimization (PSO) with a differential evolution (DE). During consecutive generations, a scale factor is produced by using a proposed mechanism based on the simulated annealing method and is applied to dynamically adjust the percentage of use of PSO and DE. In addition, the mutation operation of DE is improved, to satisfy that the proposed algorithm has different mutation operation in different searching stage. As a result, the capability of the local searching is enhanced and the prematurity of the population is restrained. The effectiveness of the proposed method has been validated through comprehensive tests using benchmark test functions. The numerical results obtained by this algorithm are compared with those obtained by the improved non-dominated sorting genetic algorithm (NSGA-II) and the other algorithms mentioned in the literature. The results show the effectiveness of the proposed MOPSDE algorithm.

Vorheriger Artikel Developing a hybrid PSO–ANN model for estimating the ultimate bearing capacity of rock-socketed piles

Nächster Artikel Myoelectric arm using artificial neural networks to reduce cognitive load of the user

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Titel: Multi-objective particle swarm-differential evolution algorithm
verfasst von: Yi-xin Su
Rui Chi
Publikationsdatum: 29.10.2015
Verlag: Springer London
Erschienen in: Neural Computing and Applications / Ausgabe 2/2017
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-015-2073-y

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