Abstract
Weighted Superposition Attraction (WSA) is a swarm based metaheuristic algorithm that is lately proposed for solving continuous optimization problems. Performance of the WSA was comprehensively tested with numerous unconstrained/constrained optimization problems. Due to its reasonable performance on continuous optimization problems we were motivated to develop a combinatorial version of WSA. A new method is devised to handle combinatorial problems without requiring an indirect representation mechanism like random keys. A random walk procedure is integrated into the combinatoric WSA (cWSA) so as to improve its diversification capability. Additionally, opposition based learning is also integrated into cWSA to increase its performance further. Performance of cWSA is tested on two well-known combinatorial optimization problems, namely the Resource Constrained Project Scheduling Problem (RCPSP) and the Permutation Flow Shop Scheduling Problem (PFSP). The results of the extensive computational study point out the efficiency of the proposed cWSA.
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