Development of a Method Based on Particle Swarm Optimization to Solve Resource Constrained Project Scheduling Problem

This work presents an efficient hybrid method based on Particle Swarm Optimization (PSO) and Termite Colony Optimization (TCO) for solving Resource Constrained Project Scheduling Problem (RCPSP). The search process of this hybrid method employs PSO iterations for global search and TCO iterations for local search. The proposed method works by interleaving the PSO and TCO search processes. The PSO method update schedules by considering the best solution found by the TCO approach. Next the TCO approach picks the solutions found by PSO search and perform local search around each solution. Each individual in TCO approach moves randomly but it is biased towards locally best observed solutions. Apart from hybridization, a new constraint handling approach is proposed to convert the infeasible solutions to the feasible ones. The standard benchmark problems of size j30, j60, j90, and j120 from PSPLIB are used to show the efficiency of the proposed method. The results showed that although PSO and TCO methods independently gives good solutions, the hybrid of PSO and TCO gives better solution compared to PSO and TCO methods.