NDLI: Multiobjective particle swarm optimization with nondominated local and global sets

Content Provider	Springer Nature Link
Author	Abido, M. A.
Copyright Year	2010
Abstract	In multiobjective particle swarm optimization (MOPSO) methods, selecting the local best and the global best for each particle of the population has a great impact on the convergence and diversity of solutions, especially when optimizing problems with high number of objectives. This paper presents an approach using two sets of nondominated solutions. The ability of the proposed approach to detect the true Pareto optimal solutions and capture the shape of the Pareto front is evaluated through experiments on well-known non-trivial multiobjective test problems as well as the real-life electric power dispatch problem. The diversity of the nondominated solutions obtained is demonstrated through different measures. The proposed approach has been assessed through a comparative study with the reported results in the literature.
Starting Page	747
Ending Page	766
Page Count	20
File Format	PDF
ISSN	15677818
Journal	Natural Computing
Volume Number	9
Issue Number	3
e-ISSN	15729796
Language	English
Publisher	Springer Netherlands
Publisher Date	2010-01-05
Publisher Place	Dordrecht
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Particle Swarm Optimization Multiobjective optimization Pareto-optimal set Nondominated solutions Statistical Physics, Dynamical Systems and Complexity Artificial Intelligence (incl. Robotics) Processor Architectures Evolutionary Biology Theory of Computation
Content Type	Text
Resource Type	Article
Subject	Computer Science Applications

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