Hybrid Evolutionary Algorithm with Adaptive Crossover, Mutation and Simulated Annealing Processes to Project Scheduling

YANNIBELLI, VIRGINIA; ANALIA ADRIANA AMANDI

Wroclaw

Conferencia; The 16th International Conference on Intelligent Data Engineering and Automated Learning; 2015

In this paper, we address a project scheduling problem that considers a priority optimization objective for project managers. This objective involves assigning the most effective set of human resources to each project activity. To solve the problem, , we propose a hybrid evolutionary algorithm. This algorithm uses adaptive crossover, mutation and simulated annealing processes in order toimprove the performance of the evolutionary search. These processes adapt theirbehavior based on the diversity of the evolutionary algorithm population. Wecompare the performance of the hybrid evolutionary algorithm with those of thealgorithms previously proposed in the literature for solving the addressedproblem. The obtained results indicate that the hybrid evolutionary algorithm significantly outperforms the previous algorithms.