The development of a sub-population genetic algorithm II (SPGA II) for multi-objective combinatorial problems

Applied Soft Computing Journal

Volumn 9, Issue 1, 2009, Pages 173-181

  Chang, Pei Chann ^a   Chen, Shih Hsin ^a  

^a YUAN ZE UNIVERSITY   (Taiwan)

Author keywords

Genetic algorithm; Multi objective optimization; Multidimensional knapsack problem; Parallel scheduling problems

Indexed keywords

ALGORITHMS; BOOLEAN FUNCTIONS; GENETIC ALGORITHMS; INTEGER PROGRAMMING; MULTIOBJECTIVE OPTIMIZATION; PARALLEL ALGORITHMS; SOLUTIONS; STAGES;

COMBINATORIAL PROBLEMS; GENETIC ALGORITHM; KNAPSACK PROBLEMS; MULTI OBJECTIVES; MULTI-OBJECTIVE PROBLEMS; MULTIDIMENSIONAL KNAPSACK PROBLEM; NSGA-II; PARALLEL SCHEDULING; PARALLEL SCHEDULING PROBLEMS; SOLUTION SPACES; TWO STAGES;

COMBINATORIAL MATHEMATICS;

EID: 53749093234     PISSN: 15684946     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.asoc.2008.04.002     Document Type: Article

References (27)

1. Deb K., Amrit Pratap S.A., and Meyarivan T. A fast and elitist multi-objective genetic algorithm-NSGA-II. Proceedings of the parallel problem solving from nature VI conference (2000) 849-858
2. Zitzler E., Laumanns M., and Bleuler S. A tutorial on evolutionary multi-objective optimization. Proceedings of the workshop on multiple objective metaheuristics (2004)
3. Affenzeller M. New Generic Hybrids Based Upon Genetic Algorithms, Institute of Systems, Science Systems Theory and Information Technology (2001), Johannes Kepler University
4. Lis J., and Eiben A.E. A multisexual genetic algorithm for multicriteria optimization. Proceedings of the 4th IEEE Conference on Evolutionary Computation (1997) 59-64
5. Cochran J.K., Horng S.M., and Fowler J.W. A multi-population genetic algorithm to solve multi-objective scheduling problems for parallel machines. Comput. Oper. Res. 30 7 (2003) 1087-1102
6. Hu J., Goodman E., Seo K., Fan Z., and Rosenberg R. The hierarchical fair competition framework for sustainable evolutionary algorithms. Evol. Comput. 13 2 (2005) 241-277
7. Coello C.A.C., Pulido G.T., and Lechuga M.S. Handling multiple objectives with particle swarm optimization. IEEE Trans. Evol. Comput. 8 3 (2004) 256-279
8. Mostaghim S., and Teich J. Covering Pareto-optimal fronts by subswarms in multi-objective particle swarm optimization. Evol. Comput. 2 (2004) 1404-1411
9. Chang P.C., Chen S.H., and Lin K.L. Two-phase sub population genetic algorithm for parallel machine-scheduling problem. Exp. Syst. Appl. 29 3 (2005) 705-712
10. Zhang Q., and Li H. MOEA/D: a multiobjective evolutionary algorithm based on decomposition. IEEE Trans. Evol. Comput. 11 6 (2007) 712-731
11. Chang P.C., Chen S.H., and Liu C.H. Sub-population genetic algorithm with mining gene structures for multi-objective flowshop scheduling problems. Exp. Syst. Appl. 33 3 (2007) 762-771
12. Tan K.C., Goh C.K., Mamun A.A., and Ei E.Z. An evolutionary artificial immune system for multi-objective optimization. Eur. J. Oper. Res. 187 2 (2008) 371-392
13. Chu P.C., and Beasley J.E. A genetic algorithm for the multidimensional knapsack problem. J. Heuristics 4 1 (1998) 63-86
14. Ishibuchi H., Kaige S., and Narukawa K. Comparison between Lamarckian and Baldwinian repair on multiobjective 0/1 knapsack problems". Proceedings of the 3rd International Conference EMO, vol. 3410 (2005) 370-385
15. Michalewicz Z., and Arabas J. Genetic algorithms for the 0/1 knapsack problem. Methodologies for Intelligent Systems (ISMIS '94), vol. 869 (1994) 134-143
16. Mumford C.L. Comparing representations and recombination operators for the multi-objective 0/1 Knapsack problem. Proceedings of the International Congress on Evolutionary Computation (2003) 854-861
17. Zydallis J.B., and Lamont G.B. Explicit building-block multiobjective evolutionary algorithms for NPC problems. Proceedings of the International Congress on Evolutionary Computation (2003) 2685-2695
18. Ishibuchi H., and Kaige S. Effects of repair procedures on the performance of EMO algorithms for multiobjective 0/1 Knapsack problems. Proceedings of the International Congress on Evolutionary Computation (2003) 2254-2261
19. Jaszkiewicz A. On the performance of multiple-objective genetic local search on the 0/1 Knapsack problem-a comparative experiment. IEEE Trans. Evol. Comput. 6 (2002) 402-412
20. Knowles J.D., and Corne D.W. A comparison of diverse approaches to mimetic multiobjective combinatorial optimization. Proceedings of the Genetic and Evolutionary Computation Conference Workshop Program (2000) 103-108
21. Knowles J.D., and Corne D.W. On metrics for comparing non-dominated sets. Proceedings of the international congress on evolutionary computation conference (CEC02) (2002), IEEE Press, New York 711-716
22. E. Zitzler, M. Laumanns, L. Thiele, SPEA 2: improving the strength Pareto evolutionary algorithm, TIK report 103, computer engineering and networks laboratory (TIK), 2001.
23. P.C. Chang, S.H. Chen, J.C. Hsieh, A global archive sub-population genetic algorithm with adaptive strategy in multi-objective parallel-machine scheduling problem, 4221, 2006, 730-739.
24. Alves M.J., and Almeida M. MOTGA: a multi-objective Tchebycheff based genetic algorithm for the multidimensional Knapsack problem. Comput. Oper. Res. 34 11 (2007) 3458-3470
25. Ishibuchi H., Yamamoto N., Murata T., and Tanaka H. Genetic algorithms and neighborhood search algorithms for fuzzy flowshop scheduling problems. Fuzzy Sets Syst. 67 1 (1994) 81-100
26. Leung Y.W., and Wang Y.P. Multiobjective programming using uniform design and genetic algorithm. IEEE Trans. Syst. Man Cybernet. C: Appl. Rev. 30 3 (2000) 293-304
27. Van Veldhuizen D.A. Multiobjective Evolutionary Algorithms: Classifications, Analyses, and New Innovations. Ph. D. thesis (1999), Graduate School of Engineering of the Air Force Institute of Technology, Air University