NDLI: Quadratic interpolation based orthogonal learning particle swarm optimization algorithm

Content Provider	Springer Nature Link
Author	Liu, Ruochen Wang, Lixia Ma, Wenping Mu, Caihong Jiao, Licheng
Copyright Year	2013
Abstract	Particle swarm optimization (PSO) is a population based algorithm for solving global optimization problems. Owing to its efficiency and simplicity, PSO has attracted many researchers’ attention and developed many variants. Orthogonal learning particle swarm optimization (OLPSO) is proposed as a new variant of PSO that relies on a new learning strategy called orthogonal learning strategy. The OLPSO differs in the utilization of the information of experience from the standard PSO, in which each particle utilizes its historical best experience and globally best experience through linear summation. In OLPSO, particles can fly in better directions by constructing an efficient exemplar through orthogonal experimental design. However, the global version based orthogonal learning PSO (OLPSO-G) still have some drawbacks in solving some complex multimodal function optimization. In this paper, we proposed a quadratic interpolation based OLPSO-G (QIOLPSO-G), in which, a quadratic interpolation based construction strategy for the personal historical best experience is applied. Meanwhile, opposition-based learning, and Gaussian mutation are also introduced into this paper to increase the diversity of the population and discourage the premature convergence. Experiments are conducted on 16 benchmark problems to validate the effectiveness of the QIOLPSO-G, and comparisons are made with four typical PSO algorithms. The results show that the introduction of the three strategies does enhance the effectiveness of the algorithm.
Starting Page	17
Ending Page	37
Page Count	21
File Format	PDF
ISSN	15677818
Journal	Natural Computing
Volume Number	13
Issue Number	1
e-ISSN	15729796
Language	English
Publisher	Springer Netherlands
Publisher Date	2013-12-22
Publisher Place	Dordrecht
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Gaussian mutation Opposition-based learning Orthogonal learning Particle swarm optimization Quadratic interpolation Theory of Computation Evolutionary Biology Processor Architectures Artificial Intelligence (incl. Robotics) Statistical Physics, Dynamical Systems and Complexity
Content Type	Text
Resource Type	Article
Subject	Computer Science Applications

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