An ABC-Genetic method to solve resource constrained project scheduling problem

The aim of this work is to study the effect of hybridization on the performance of the Artificial Bee Colony (ABC) as a recently introduced metaheuristic for solving Resource Constrained Project Scheduling Problem (RCPSP). For this purposes, the ABC is combined with a Genetic Algorithm (GA). At the initial time, the algorithm generates a set of schedules randomly. The initial solution has been evaluated against constraints, and the infeasible solutions have been resolved to feasible ones. Then, the initial schedules are to be improved iteratively using a hybrid method until termination condition is met. The proposed method works by integrating the ABC and GA search processes. The GA method updates schedules by including the best solutions found by the ABC approach. Next, the ABC method picks the solutions found by GA search. The new methodological approach is used by the algorithm to maintain the priorities of the activities in feasible ranges. The performance of the proposed algorithm has been compared against a set of state-of-art algorithms. The simulation results have demonstrated that the proposed algorithm provides an efficient way for solving RCPSP and produce competitive results compared to other algorithms investigated in this work.