Solving Constrained Global Optimization Problems by Using Hybrid Evolutionary Computing and Artificial Life Approaches
Author
Source
Mathematical Problems in Engineering
Issue
Vol. 2012, Issue 2012 (31 Dec. 2012), pp.1-36, 36 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2012-08-29
Country of Publication
Egypt
No. of Pages
36
Main Subjects
Abstract EN
This work presents a hybrid real-coded genetic algorithm with a particle swarm optimization (RGA-PSO) algorithm and a hybrid artificial immune algorithm with a PSO (AIA-PSO) algorithm for solving 13 constrained global optimization (CGO) problems, including six nonlinear programming and seven generalized polynomial programming optimization problems.
External RGA and AIA approaches are used to optimize the constriction coefficient, cognitive parameter, social parameter, penalty parameter, and mutation probability of an internal PSO algorithm.
CGO problems are then solved using the internal PSO algorithm.
The performances of the proposed RGA-PSO and AIA-PSO algorithms are evaluated using 13 CGO problems.
Moreover, numerical results obtained using the proposed RGA-PSO and AIA-PSO algorithms are compared with those obtained using published individual GA and AIA approaches.
Experimental results indicate that the proposed RGA-PSO and AIA-PSO algorithms converge to a global optimum solution to a CGO problem.
Furthermore, the optimum parameter settings of the internal PSO algorithm can be obtained using the external RGA and AIA approaches.
Also, the proposed RGA-PSO and AIA-PSO algorithms outperform some published individual GA and AIA approaches.
Therefore, the proposed RGA-PSO and AIA-PSO algorithms are highly promising stochastic global optimization methods for solving CGO problems.
American Psychological Association (APA)
Wu, Jui-Yu. 2012. Solving Constrained Global Optimization Problems by Using Hybrid Evolutionary Computing and Artificial Life Approaches. Mathematical Problems in Engineering،Vol. 2012, no. 2012, pp.1-36.
https://search.emarefa.net/detail/BIM-1002004
Modern Language Association (MLA)
Wu, Jui-Yu. Solving Constrained Global Optimization Problems by Using Hybrid Evolutionary Computing and Artificial Life Approaches. Mathematical Problems in Engineering No. 2012 (2012), pp.1-36.
https://search.emarefa.net/detail/BIM-1002004
American Medical Association (AMA)
Wu, Jui-Yu. Solving Constrained Global Optimization Problems by Using Hybrid Evolutionary Computing and Artificial Life Approaches. Mathematical Problems in Engineering. 2012. Vol. 2012, no. 2012, pp.1-36.
https://search.emarefa.net/detail/BIM-1002004
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1002004