Evolutionary Algorithms for Solving Multi-Objective Problems
Evolutionary Algorithms for Solving Multi-Objective Problems
Combining convergence and diversity in evolutionary multiobjective optimization
Evolutionary Computation
Parto-Optimal Solutions for Multi-objective Production Scheduling Problems
EMO '01 Proceedings of the First International Conference on Evolutionary Multi-Criterion Optimization
A Hybrid Evolutionary Approach for Multicriteria Optimization Problems: Application to the Flow Shop
EMO '01 Proceedings of the First International Conference on Evolutionary Multi-Criterion Optimization
Comparison of heuristics for flowtime minimisation in permutation flowshops
Computers and Operations Research
Expert Systems with Applications: An International Journal
Computers and Operations Research
A Review and Evaluation of Multiobjective Algorithms for the Flowshop Scheduling Problem
INFORMS Journal on Computing
Expert Systems with Applications: An International Journal
A multi-objective genetic local search algorithm and itsapplication to flowshop scheduling
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Evolutionary Computation
MOEA/D: A Multiobjective Evolutionary Algorithm Based on Decomposition
IEEE Transactions on Evolutionary Computation
A Hybrid Quantum-Inspired Genetic Algorithm for Multiobjective Flow Shop Scheduling
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
An adaptive annealing genetic algorithm for the job-shop planning and scheduling problem
Expert Systems with Applications: An International Journal
Computational Optimization and Applications
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The permutation flow shop scheduling problem is addressed in this paper. Two objectives, minimization of makespan and total flow time, are considered. We propose a memetic algorithm, called NNMA, by integrating a general multiobjective evolutionary algorithm (NSGA-II) with a problem-specific heuristic (NEH). We take NEH as a local improving procedure in NNMA and propose several adaptations including the acceptance criterion and job-insertion ordering to deal with multiple objectives and to improve its performance. We test the performance of NNMA using 90 public problem instances with different problem scales, and compare its performance with 23 algorithms. The experimental results show that our NNMA provides close performance for 30 small-scale instances and better performance for 50 medium- and large-scale instances. Furthermore, more than 70% of the net set of non-dominated solutions is updated by NNMA for these 50 instances.