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Solving Unconstrained Global Optimization Problems via Hybrid Swarm Intelligence Approaches
Author
Source
Mathematical Problems in Engineering
Issue
Vol. 2013, Issue 2013 (31 Dec. 2013), pp.1-15, 15 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2013-03-14
Country of Publication
Egypt
No. of Pages
15
Main Subjects
Abstract EN
Stochastic global optimization (SGO) algorithms such as the particle swarm optimization (PSO) approach have become popular for solving unconstrained global optimization (UGO) problems.
The PSO approach, which belongs to the swarm intelligence domain, does not require gradient information, enabling it to overcome this limitation of traditional nonlinear programming methods.
Unfortunately, PSO algorithm implementation and performance depend on several parameters, such as cognitive parameter, social parameter, and constriction coefficient.
These parameters are tuned by using trial and error.
To reduce the parametrization of a PSO method, this work presents two efficient hybrid SGO approaches, namely, a real-coded genetic algorithm-based PSO (RGA-PSO) method and an artificial immune algorithm-based PSO (AIA-PSO) method.
The specific parameters of the internal PSO algorithm are optimized using the external RGA and AIA approaches, and then the internal PSO algorithm is applied to solve UGO problems.
The performances of the proposed RGA-PSO and AIA-PSO algorithms are then evaluated using a set of benchmark UGO problems.
Numerical results indicate that, besides their ability to converge to a global minimum for each test UGO problem, the proposed RGA-PSO and AIA-PSO algorithms outperform many hybrid SGO algorithms.
Thus, the RGA-PSO and AIA-PSO approaches can be considered alternative SGO approaches for solving standard-dimensional UGO problems.
American Psychological Association (APA)
Wu, Jui-Yu. 2013. Solving Unconstrained Global Optimization Problems via Hybrid Swarm Intelligence Approaches. Mathematical Problems in Engineering،Vol. 2013, no. 2013, pp.1-15.
https://search.emarefa.net/detail/BIM-1008827
Modern Language Association (MLA)
Wu, Jui-Yu. Solving Unconstrained Global Optimization Problems via Hybrid Swarm Intelligence Approaches. Mathematical Problems in Engineering No. 2013 (2013), pp.1-15.
https://search.emarefa.net/detail/BIM-1008827
American Medical Association (AMA)
Wu, Jui-Yu. Solving Unconstrained Global Optimization Problems via Hybrid Swarm Intelligence Approaches. Mathematical Problems in Engineering. 2013. Vol. 2013, no. 2013, pp.1-15.
https://search.emarefa.net/detail/BIM-1008827
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1008827