Population recombination strategies for multi-objective particle swarm optimization

Population recombination strategies for multi-objective particle swarm optimization 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. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Soft Computing Springer Journals

Population recombination strategies for multi-objective particle swarm optimization

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Publisher
Springer Berlin Heidelberg
Copyright
Copyright © 2016 by Springer-Verlag Berlin Heidelberg
Subject
Engineering; Computational Intelligence; Artificial Intelligence (incl. Robotics); Mathematical Logic and Foundations; Control, Robotics, Mechatronics
ISSN
1432-7643
eISSN
1433-7479
D.O.I.
10.1007/s00500-016-2078-1
Publisher site
See Article on Publisher Site

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