Multi-objective genetic algorithm and its applications to flowshop scheduling
Computers and Industrial Engineering
Genetic algorithms for flowshop scheduling problems
Computers and Industrial Engineering
Quality Engineering Using Robust Design
Quality Engineering Using Robust Design
Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Evolutionary Algorithms for Solving Multi-Objective Problems (Genetic and Evolutionary Computation)
Evolutionary Algorithms for Solving Multi-Objective Problems (Genetic and Evolutionary Computation)
A discrete version of particle swarm optimization for flowshop scheduling problems
Computers and Operations Research
A discrete particle swarm optimization algorithm for the no-wait flowshop scheduling problem
Computers and Operations Research
A combinatorial particle swarm optimisation for solving permutation flowshop problems
Computers and Industrial Engineering
An improved particle swarm optimization algorithm for flowshop scheduling problem
Information Processing Letters
Quality Assessment of Pareto Set Approximations
Multiobjective Optimization
Nonlinear inertia weight variation for dynamic adaptation in particle swarm optimization
Computers and Operations Research
Expert Systems with Applications: An International Journal
Bi-objective group scheduling in hybrid flexible flowshop: A multi-phase approach
Expert Systems with Applications: An International Journal
New factorial design theoretic crossover operator for parametrical problem
EuroGP'03 Proceedings of the 6th European conference on Genetic programming
A multi-objective genetic local search algorithm and itsapplication to flowshop scheduling
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Multiobjective evolutionary algorithms: a comparative case studyand the strength Pareto approach
IEEE Transactions on Evolutionary Computation
Performance assessment of multiobjective optimizers: an analysis and review
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Evolutionary Computation
Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients
IEEE Transactions on Evolutionary Computation
Hybrid Taguchi-genetic algorithm for global numerical optimization
IEEE Transactions on Evolutionary Computation
Multiobjective GAs, quantitative indices, and pattern classification
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A Hybrid Quantum-Inspired Genetic Algorithm for Multiobjective Flow Shop Scheduling
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
An Effective PSO-Based Hybrid Algorithm for Multiobjective Permutation Flow Shop Scheduling
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Tuning the structure and parameters of a neural network by using hybrid Taguchi-genetic algorithm
IEEE Transactions on Neural Networks
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A hybrid sliding level Taguchi-based particle swarm optimization (HSLTPSO) algorithm is proposed for solving multi-objective flowshop scheduling problems (FSPs). The proposed HSLTPSO integrates particle swarm optimization, sliding level Taguchi-based crossover, and elitist preservation strategy. The novel contribution of the proposed HSLTPSO is the use of a PSO to explore the optimal feasible region in macro-space, the use of a systematic reasoning mechanism of the sliding level Taguchi-based crossover to exploit the better solution in micro-space, and the use of the elitist preservation strategy to retain the best particles of multi-objective population for next iteration. The sliding level Taguchi-based crossover is embedded in the PSO to find the best solutions and consequently enhance the PSO. Using the systematic reasoning way of the Taguchi-based crossover with considering the influence of tuning factors @a, @b and @c is presented in this study to solve the conflicting problem of non-feasible solutions and to find the better particles. As a result, it exhibits a significant improvement in Pareto best solutions of the FSP. By combining the advantages of exploration and exploitation, from the computational experiments of the six test problems, the HSLTPSO provides better results compared to the existing methods reported in the literature when solving multi-objective FSPs. Therefore, the HSLTPSO is an effective approach in solving multi-objective FSPs.