Abstract
Ant colony optimization is a meta-heuristic that has been widely used for solving combinatorial optimization problems, and most real-world applications are concerned with multi-objective optimization problems. The Pareto strength ant colony optimization (PSACO) algorithm, which uses the concepts of Pareto optimality and also the domination concept, has been shown to be very effective in optimizing any number of objectives simultaneously. This paper modifies the PSACO algorithm to solve two combinatorial optimization problems: the travelling salesman problem (TSP); and the job-shop scheduling problem (JSSP). It uses the random-weight based method as an improvement. The proposed method achieved a better performance than the original PSACO algorithm for both combinatorial optimization problems and obtained well-distributed Pareto-optimal fronts.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
