On the edge of feasibility: a case study of the particle swarm optimizer

Bonyadi, Mohammad Reza and Michalewicz, Zbigniew (2014). On the edge of feasibility: a case study of the particle swarm optimizer. In: 2014 IEEE Congress On Evolutionary Computation (CEC). IEEE Congress on Evolutionary Computation (CEC), Beijing, Peoples R China, (3059-3066). 6-11 July 2014. doi:10.1109/CEC.2014.6900343

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Author Bonyadi, Mohammad Reza
Michalewicz, Zbigniew
Title of paper On the edge of feasibility: a case study of the particle swarm optimizer
Conference name IEEE Congress on Evolutionary Computation (CEC)
Conference location Beijing, Peoples R China
Conference dates 6-11 July 2014
Proceedings title 2014 IEEE Congress On Evolutionary Computation (CEC)
Journal name Proceedings of the 2014 IEEE Congress on Evolutionary Computation, CEC 2014
Place of Publication Piscataway, NJ, United States
Publisher IEEE
Publication Year 2014
Year available 2014
Sub-type Fully published paper
DOI 10.1109/CEC.2014.6900343
Open Access Status File (Author Post-print)
ISBN 9781479914883
Start page 3059
End page 3066
Total pages 8
Language eng
Abstract/Summary In many real-world constrained optimization problems (COPs) it is highly probable that some constraints are active at optimum points, i.e. some optimum points are boundary points between feasible and infeasible parts of the search space. A method is proposed which narrows the feasible area of a COP to its boundary. In the proposed method the thickness of the narrowed boundary is adjustable by a parameter. The method is extended in a way that it is able to limit the feasible regions to boundaries where at least one of the constraints in a given subset of all constraints is active and the remaining constraints might be active or not. Another extension is able to limit the search to cases where all constraints in a given subset are active and the rest might be active or not. The particle swarm optimization algorithm is used as a framework to compare the proposed methods. Results show that the proposed methods can limit the search to the requested boundary and they are effective in locating optimal solutions on the boundaries of the feasible and infeasible area.
Keyword Computer Science, Artificial Intelligence
Computer Science, Theory & Methods
Engineering, Electrical & Electronic
Computer Science
Engineering
Q-Index Code E1
Q-Index Status Provisional Code
Institutional Status Non-UQ

Document type: Conference Paper
Sub-type: 2014 Ieee Congress On Evolutionary Computation (Cec)
Collection: Centre for Advanced Imaging Publications
 
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