Skip to Main Content
An improved differential evolution (IDE) algorithm that adopts a novel mutation strategy to speed up the convergence rate is introduced to solve the resource-constrained project scheduling problem (RCPSP) with the objective of minimizing project duration. Activities priorities for scheduling are represented by individual vectors and a serial scheme is utilized to transform the individual-represented priorities to a feasible schedule according to the precedence and resource constraints so as to be evaluated. To investigate the performance of the IDE-based approach for the RCPSP, it is compared against the meta-heuristic methods of hybrid genetic algorithm (HGA), particle swarm optimization (PSO) and several well selected heuristics. The results show that the proposed scheduling method is better than general heuristic rules and is able to obtain the same optimal result as the HGA and PSO approaches but more efficient than the two algorithms.