An Improved PSO Algorithm for Resource-Constrained Project Scheduling Problem

An improved Particle Swarm Optimization (PSO) algorithm for resource-constrained project scheduling problem is proposed. The particle presentation, encoding scheme and decoding rule of this algorithm are presented. Improvements based on the basic PSO include: the particle swarm is initialized by heuristic rule to improve the quality of particles; inertia weight is self-adapted with iteration of the algorithm to decelerate the speed of particles; crossover mechanism of genetic algorithm are applied to particle swarm to enable the exchange of good characteristics between two particles. Computational results for project instances of PSPLIB demonstrate that this improved PSO is effective compared with other mataheuristic approaches.