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Multipopulation Ensemble Particle Swarm Optimizer for Engineering Design Problems
Joint Authors
Source
Mathematical Problems in Engineering
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-30, 30 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-11-11
Country of Publication
Egypt
No. of Pages
30
Main Subjects
Abstract EN
Particle swarm optimization (PSO) is an efficient optimization algorithm and has been applied to solve various real-world problems.
However, the performance of PSO on a specific problem highly depends on the velocity updating strategy.
For a real-world engineering problem, the function landscapes are usually very complex and problem-specific knowledge is sometimes unavailable.
To respond to this challenge, we propose a multipopulation ensemble particle swarm optimizer (MPEPSO).
The proposed algorithm consists of three existing efficient and simple PSO searching strategies.
The particles are divided into four subpopulations including three indicator subpopulations and one reward subpopulation.
Particles in the three indicator subpopulations update their velocities by different strategies.
During every learning period, the improved function values of the three strategies are recorded.
At the end of a learning period, the reward subpopulation is allocated to the best-performed strategy.
Therefore, the appropriate PSO searching strategy can have more computational expense.
The performance of MPEPSO is evaluated by the CEC 2014 test suite and compared with six other efficient PSO variants.
These results suggest that MPEPSO ranks the first among these algorithms.
Moreover, MPEPSO is applied to solve four engineering design problems.
The results show the advantages of MPEPSO.
The MATLAB source codes of MPEPSO are available at https://github.com/zi-ang-liu/MPEPSO.
American Psychological Association (APA)
Liu, Ziang& Nishi, Tatsushi. 2020. Multipopulation Ensemble Particle Swarm Optimizer for Engineering Design Problems. Mathematical Problems in Engineering،Vol. 2020, no. 2020, pp.1-30.
https://search.emarefa.net/detail/BIM-1193247
Modern Language Association (MLA)
Liu, Ziang& Nishi, Tatsushi. Multipopulation Ensemble Particle Swarm Optimizer for Engineering Design Problems. Mathematical Problems in Engineering No. 2020 (2020), pp.1-30.
https://search.emarefa.net/detail/BIM-1193247
American Medical Association (AMA)
Liu, Ziang& Nishi, Tatsushi. Multipopulation Ensemble Particle Swarm Optimizer for Engineering Design Problems. Mathematical Problems in Engineering. 2020. Vol. 2020, no. 2020, pp.1-30.
https://search.emarefa.net/detail/BIM-1193247
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1193247