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Please use this identifier to cite or link to this item: http://repository.iitr.ac.in/handle/123456789/9901
Title: A Survey on Parallel Particle Swarm Optimization Algorithms
Authors: Lalwani S.
Sharma H.
Satapathy S.C.
Deep K.
Bansal J.C.
Published in: Arabian Journal for Science and Engineering
Abstract: Most of the complex research problems can be formulated as optimization problems. Emergence of big data technologies have also commenced the generation of complex optimization problems with large size. The high computational cost of these problems has rendered the development of optimization algorithms with parallelization. Particle swarm optimization (PSO) algorithm is one of the most popular swarm intelligence-based algorithm, which is enriched with robustness, simplicity and global search capabilities. However, one of the major hindrance with PSO is its susceptibility of getting entrapped in local optima and; alike other evolutionary algorithms the performance of PSO gets deteriorated as soon as the dimension of the problem increases. Hence, several efforts are made to enhance its performance that includes the parallelization of PSO. The basic architecture of PSO inherits a natural parallelism, and receptiveness of fast processing machines has made this task pretty convenient. Therefore, parallelized PSO (PPSO) has emerged as a well-accepted algorithm by the research community. Several studies have been performed on parallelizing PSO algorithm so far. Proposed work presents a comprehensive and systematic survey of the studies on PPSO algorithms and variants along with their parallelization strategies and applications. © 2019, King Fahd University of Petroleum & Minerals.
Citation: Arabian Journal for Science and Engineering (2019), 44(4): 2899-2923
URI: https://doi.org/10.1007/s13369-018-03713-6
http://repository.iitr.ac.in/handle/123456789/9901
Issue Date: 2019
Publisher: Springer Verlag
Keywords: GPU
Large-size complex optimization problems
MPI
Parallel computing
Particle swarm optimization
Swarm intelligence-based algorithm
ISSN: 2193567X
Author Scopus IDs: 55772632500
56229215400
16643664100
8561208900
57189656835
Author Affiliations: Lalwani, S., Department of Computer Science and Engineering, Rajasthan Technical University, Kota, India
Sharma, H., Department of Computer Science and Engineering, Rajasthan Technical University, Kota, India
Satapathy, S.C., School of Computer Engineering, Kalinga Institute of Industrial Technology, Bhubaneswar, Odisha, India
Deep, K., Department of Mathematics, Indian Institute of Technology, Roorkee, India
Bansal, J.C., South Asian University, New Delhi, India
Funding Details: Acknowledgements The first author (S.L.) gratefully acknowledges Science & Engineering Research Board, DST, Government of India, for the fellowship (PDF/2016/000008).
Corresponding Author: Lalwani, S.; Department of Computer Science and Engineering, Rajasthan Technical UniversityIndia; email: slalwani.math@gmail.com
Appears in Collections:Journal Publications [MA]

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