A distributed coevolutionary algorithm for multiobjective hybrid flowshop scheduling problems

A distributed coevolutionary algorithm is proposed to solve the multiobjective hybrid flowshop scheduling problems to minimize the maximum completion time and total tardiness of jobs. The framework of the distributed coevolutionary algorithm consists of a global agent and multiple local agents. The global agent and local agents evolve independently and cooperate by interchanging a selected solution list. Unlike the cooperative coevolutionary algorithms in the literature, the proposed algorithm does not decompose the scheduling problem and executes evolutionary operations based on the whole solution of the problem in all the agents. SPEA2 is the core components in the local agents. Path relinking is applied in order to implement the evolutionary computation among non-dominated solutions in the global agent. We analyzed the time complexity of the proposed algorithm. To evaluate the performance against the benchmark of multiobjective evolutionary algorithms, it is tested on a large number of computational instances. The computational experiments show the proposed distributed coevolutionary algorithm can obtain better solution quality than other algorithms within given computational time.