Particle Swarm and Ant Colony Algorithms Hybridized for Multi-Mode Resource-constrained Project Scheduling Problem with Minimum Time Lag

Abstract

MMRCPSP/MIN has been proved to be a NP-hard problem. Based on the introduction of model of project scheduling problem with minimum time lag developed by Roland Heilmann, the paper analyzed two swarm intelligence algorithms which have good performance in solving combinatorial optimization problem-ACO and PSO algorithms. Based on analysis of these algorithms' defects, the paper proposed that the defects of ACO algorithm such as hard to converge, performance significantly affected by parameters, high requirement for parameters and so on, can be made up by the high convergence of PSO algorithm. The parameters of ACO algorithm were set in terms of the solution of PSO algorithm. Through particle swarm iteration and making convergence effect and better objective solution of ACO algorithm as fitness value in order to lead the optimization of particle swarm, we can obtain the optimal solution as well as better convergence speed and effect. Finally, the model and problem solving process were programmed in the C++ language. Intensive computational experiments were done on cases in PSPLIB. The result shows that with the iteration of PSO algorithm, both the performance and convergence of ACO algorithm are improved.