Improved Grey Wolf Optimization for Solving Multi-objective Flow Shop Scheduling Problem
To efficiently solve the multi-objective permutation flow shop scheduling problems,this paper proposes an improved multi-objective gray wolf optimization algorithm.The algorithm introduces a hybridization strategy which combining multi-stage search strategy with walk guard strategy to enhance the search capabilities of the algorithm.At the same time,it presents a discrete individual updating method based on crossover operation to make the algorithm suitable for the multi-objective discrete scheduling problem.The experimental results show that the proposed algorithm can improve search efficiency of optimization and find more ap-proximate Pareto optimal solutions.
flow shop schedulingmulti-objective optimizationgray wolf optimization
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