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18.02.2016 | Methodologies and Application

Population recombination strategies for multi-objective particle swarm optimization

verfasst von: Li Ming Zheng, Qiang Wang, Sheng Xin Zhang, Shao Yong Zheng

Erschienen in: Soft Computing | Ausgabe 16/2017

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Abstract

Multi-objective particle swarm optimization algorithm (MOPSOs) has been found to exhibit fast convergence speed but with high probability to fall into local optimum. To overcome this shortcoming, a population recombination strategy is combined with a new mutation strategy to strengthen the ability to jump out of local optimum. From the investigation conducted, it can be found that, when the MOPSO falls into local optimum, the population will stop producing effective particles to update the archive. Population recombination strategy, which utilizes the information of the best variable found so far to construct the new population. This can increase the probability for population to approach the Pareto optimal front, while additional mutation operation can enhance the diversity of population. Experimental study on the bi-objective and three-objective benchmark problems shows that the MOPSO based on proposed strategies is superior to previous multi-objective algorithms in the literature.

Vorheriger Artikel Improving the multiobjective evolutionary algorithm based on decomposition with new penalty schemes

Nächster Artikel Detecting structural breaks in time series via genetic algorithms

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Titel: Population recombination strategies for multi-objective particle swarm optimization
verfasst von: Li Ming Zheng
Qiang Wang
Sheng Xin Zhang
Shao Yong Zheng
Publikationsdatum: 18.02.2016
Verlag: Springer Berlin Heidelberg
Erschienen in: Soft Computing / Ausgabe 16/2017
Print ISSN: 1432-7643
Elektronische ISSN: 1433-7479
DOI: https://doi.org/10.1007/s00500-016-2078-1

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