A hybrid genetic algorithm for flexible task collaborative scheduling

Flexible job scheduling is considered a NP-hard problem (FIS). A hybrid algorithm based on Genetic Algorithm (GA) and Simulated Annealing (SA) is proposed, which is used to schedule the tasks. A two dimensional matrix encoding is adopted, row operator and column operator are advanced accordingly, column crossover operator and column mutation are chosen by considering the Constraints. Elitist selection strategy is employed for accelerating the colony convergence. Capabilities and other factors which would influence the design results are considered when creating individual. Time scheduling and optimization are implemented in decoding phase. Finally, a simulation experiment is carried out by using the proposed algorithm, and comparison with the other algorithm is implemented, it is showed that the convergent velocity is fast and the search ability is better.