Genetic algorithms and neighborhood search algorithms for fuzzy flowshop scheduling problems
Fuzzy Sets and Systems - Special issue on operations research
A Genetic Algorithm for the Multidimensional Knapsack Problem
Journal of Heuristics
Genetic Algorithms for the 0/1 Knapsack Problem
ISMIS '94 Proceedings of the 8th International Symposium on Methodologies for Intelligent Systems
Computers and Operations Research
The Hierarchical Fair Competition (HFC) Framework for Sustainable Evolutionary Algorithms
Evolutionary Computation
Expert Systems with Applications: An International Journal
Computers and Operations Research
Two-phase sub population genetic algorithm for parallel machine-scheduling problem
Expert Systems with Applications: An International Journal
ICNC'06 Proceedings of the Second international conference on Advances in Natural Computation - Volume Part I
Comparison between lamarckian and baldwinian repair on multiobjective 0/1 knapsack problems
EMO'05 Proceedings of the Third international conference on Evolutionary Multi-Criterion Optimization
Multiobjective programming using uniform design and genetic algorithm
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
IEEE Transactions on Evolutionary Computation
Handling multiple objectives with particle swarm optimization
IEEE Transactions on Evolutionary Computation
MOEA/D: A Multiobjective Evolutionary Algorithm Based on Decomposition
IEEE Transactions on Evolutionary Computation
Multi-objective genetic-based algorithms for a cross-docking scheduling problem
Applied Soft Computing
Journal of Intelligent Manufacturing
Modeling and Pareto optimization of multi-objective order scheduling problems in production planning
Computers and Industrial Engineering
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Previous research has shown that sub-population genetic algorithm is effective in solving the multi-objective combinatorial problems. Based on these pioneering efforts, this paper extends the SPGA algorithm with a global Pareto archive technique and a two-stage approach to solve the multi-objective problems. In the first stage, the areas next to the two single objectives are searched and solutions explored around these two extreme areas are reserved in the global archive for later evolutions. Then, in the second stage, larger searching areas except the middle area are further extended to explore the solution space in finding the near-optimal frontiers. Through extensive experimental results, SPGA II does outperform SPGA, NSGA II, and SPEA 2 in the parallel scheduling problems and knapsack problems; it shows that the approach improves the sub-population genetic algorithm significantly. It may be of interests for researchers in solving multi-objective combinatorial problems.