On the performance of bee algorithms for resource-constrained project scheduling problem

This work investigates the application of bee algorithms for resource constrained project scheduling problem (or RCPSP). Three methods are developed based on recently introduced bee algorithms known as bee algorithm (BA), artificial bee colony (ABC), and bee swarm optimization (BSO). The proposed algorithms iteratively solve the RCPSP by utilizing intelligent behaviors of honey bees. Each algorithm has three main phases: initialization, update, and termination. At first phase, a set of schedules are generated randomly in order to initialize the population of the algorithms. Then, the initial population will be improved iteratively until termination condition is met. The update phase constitutes the body of each algorithm. Each algorithm uses different types of bees to provide appropriate level of exploration over search space while maintaining exploitation of good solutions. Three new local search methods are incorporated into the proposed methods in order to find more efficiency. Also, an efficient constraint handling method is introduced to resolve the infeasible solutions. The performances of the proposed algorithms are compared against a set of state-of-art algorithms. The simulation results showed that bee algorithms provides an efficient way for solving RCPSP and produce competitive results compared to other algorithms investigated in this work.