A Depth-First Mutation-Based Genetic Algorithm for Flow Shop Scheduling Problems

Abstract

This paper presents a novel memetic genetic algorithm (GA) for the flow shop scheduling problem by combining mutation-based local search with traditional genetic algorithm. The local search is based on the depth-first mutation-based searching process and the depth, i. e., the number of total mutation within each generation is according to the number of jobs to be scheduled. In traditional GA, the optimal solution may just next to the current best one however the combination of crossover and mutation may generate individuals with the solution jumping off the optimal zones. Therefore, in this research the classical mutation is replaced by depth-first multiple mutations within each generation. The multi-mutation can provide a more completely deep searching during each generation therefore there are more chances for the evolving searching procedure to reach to the optimal zone. In addition, the SA based acceptance rate is designed to be incorporated into the searching procedure; therefore the convergence rate of the hybrid GA can be further improved. The test problems are selected from the OR library, and the computational results show that the hybrid GA has a better solution quality than simple GA and NEH heuristic