A hybrid metaheuristic for the resource-constrained project scheduling problem

Abstract

This study presents a hybrid metaheuristic ANGEL for the resource-constrained project scheduling problem (RCPSP). ANGEL combines ant colony optimization (ACO), genetic algorithm (GA) and local search strategy. The procedures of ANGEL are as follows. First, ACO searches the solution space and generates activity lists to provide the initial population for GA. Next, GA is executed and the pheromone set in ACO is updated when GA obtains a better solution. When GA terminates, ACO searches again by using a new pheromone set. ACO and GA search alternately and cooperatively in the solution space. This study also proposes an efficient local search procedure which is applied to yield a better solution when ACO or GA obtains a solution. A final search is applied upon the termination of ACO and GA. The experimental results of ANGEL on the standard sets of the project instances show that ANGEL is an effective method for solving the RCPSP.