Sequential Hybrid Particle Swarm Optimization and Gravitational Search Algorithm with Dependent Random Coefficients
Joint Authors
Chao-Long, Zhang
Jiang, Shanhe
Chen, Shijun
Source
Mathematical Problems in Engineering
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-17, 17 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-04-21
Country of Publication
Egypt
No. of Pages
17
Main Subjects
Abstract EN
Particle swarm optimization (PSO) has been proven to show good performance for solving various optimization problems.
However, it tends to suffer from premature stagnation and loses exploration ability in the later evolution period when solving complex problems.
This paper presents a sequential hybrid particle swarm optimization and gravitational search algorithm with dependent random coefficients called HPSO-GSA, which first incorporates the gravitational search algorithm (GSA) with the PSO by means of a sequential operating mode and then adopts three learning strategies in the hybridization process to overcome the aforementioned problem.
Specifically, the particles in the HPSO-GSA enter into the PSO stage and update their velocities by adopting the dependent random coefficients strategy to enhance the exploration ability.
Then, the GSA is incorporated into the PSO by using fixed iteration interval cycle or adaptive evolution stagnation cycle strategies when the swarm drops into local optimum and fails to improve their fitness.
To evaluate the effectiveness and feasibility of the proposed HPSO-GSA, the simulations were conducted on benchmark test functions.
The results reveal that the HPSO-GSA exhibits superior performance in terms of accuracy, reliability, and efficiency compared to PSO, GSA, and other recently developed hybrid variants.
American Psychological Association (APA)
Jiang, Shanhe& Chao-Long, Zhang& Chen, Shijun. 2020. Sequential Hybrid Particle Swarm Optimization and Gravitational Search Algorithm with Dependent Random Coefficients. Mathematical Problems in Engineering،Vol. 2020, no. 2020, pp.1-17.
https://search.emarefa.net/detail/BIM-1193629
Modern Language Association (MLA)
Jiang, Shanhe…[et al.]. Sequential Hybrid Particle Swarm Optimization and Gravitational Search Algorithm with Dependent Random Coefficients. Mathematical Problems in Engineering No. 2020 (2020), pp.1-17.
https://search.emarefa.net/detail/BIM-1193629
American Medical Association (AMA)
Jiang, Shanhe& Chao-Long, Zhang& Chen, Shijun. Sequential Hybrid Particle Swarm Optimization and Gravitational Search Algorithm with Dependent Random Coefficients. Mathematical Problems in Engineering. 2020. Vol. 2020, no. 2020, pp.1-17.
https://search.emarefa.net/detail/BIM-1193629
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1193629