A Hybrid Metaheuristic ACO-GA with an Application in Sports Competition Scheduling

This paper presents a hybrid metaheuristic ACO-GA for the problem of sports competition scheduling (SCS). ACO-GA combines ant colony optimization (ACO) and genetic algorithms (GA). The procedures of ACO-GA are as follows. First, GA searches the solution space and generates activity lists to provide the initial population for ACO. Next, ACO is executed, when ACO terminates, the crossover and mutation operations of GA generate new population. ACO and GA search alternately and cooperatively in the solution space. Then we test ACO-GA with Oliver30 and att48. The results indicate that ACO-GA is an effective method. Finally this paper deals with SCS using ACO-GA.