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The flexible flow shop scheduling problem is an NP-hard problem and itrequires significant resolution time to find optimal or even adequate solutionswhen dealing with large size instances. Thus, this paper proposes a dual islandgenetic algorithm consisting of a parallel cellular model and a parallel pseudomodel. This is a two-level parallelization highly consistent with theunderlying architecture and is well suited for parallelizing inside or betweenGPUs and a multi-core CPU. At the higher level, the efficiency of island GAs isimproved by exploring new regions within the search space utilizing differentmethods. In the meantime, the cellular model keeps the population diversity bydecentralization and the pseudo model enhances the search ability by thecomplementary parent strategy at the lower level. To encourage the informationsharing between islands, a penetration inspired migration policy is designedwhich sets the topology, the rate, the interval and the strategy adaptively.Finally, the proposed method is tested on some large size flexible flow shopscheduling instances in comparison with other parallel algorithms. Thecomputational results show that it cannot only obtain competitive results butalso reduces execution time.

A Dual Heterogeneous Island Genetic Algorithm for Solving Large Size  Flexible Flow Shop Scheduling Problems on Hybrid multi-core CPU and GPU  Platforms