This paper presents a multi-objective constraint handling method incorporating the Particle Swarm Optimization (PSO) algorithm. The proposed approach adopts a concept of Pareto domination from multi-objective optimization, and uses a few selection rules to determine particles’ behaviors to guide the search direction. A goal-oriented programming concept is adopted to improve efficiency. Diversity is maintained by perturbing particles with a small probability. The simulation results on the three engineering benchmark problems demonstrate the proposed approach is highly competitive.
Funding
Category 1 - Australian Competitive Grants (this includes ARC, NHMRC)
History
Start Page
1528
End Page
1535
Number of Pages
8
Start Date
2008-01-01
ISBN-13
9781424418237
Location
Hong Kong, China
Publisher
IEEE
Place of Publication
USA
Peer Reviewed
Yes
Open Access
No
External Author Affiliations
Faculty of Business and Informatics; RMIT University; TBA Research Institute;