An Improved Multi-objective Evolutionary Algorithm with the Hybrid Strategies
Автор: Gao Guibing, Huang Gang, Zhang Guojun
Журнал: International Journal of Education and Management Engineering(IJEME) @ijeme
Статья в выпуске: 3 vol.1, 2011 года.
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An improved multi-objective evolutionary algorithm with the hybrid strategies is presented in this paper for multi-objective optimization problems. The evolution process is divided into initial exploration stage, the middle feedback stage and the accelerating convergence stage by the amount of non-dominated individuals in the population. The hybrid strategies and adaptive population structure are employed to improve the behavior of the algorithm at the different stages. The proposed algorithm is validated by 3 benchmark test problems. Compared with three other famous multi-objective algorithms by two quality indicators, the proposed algorithm achieves competitive results.
Evolutionary algorithm, multi-objective optimization, hybrid, adaptive, non-dominated solution
Короткий адрес: https://sciup.org/15013598
IDR: 15013598
Список литературы An Improved Multi-objective Evolutionary Algorithm with the Hybrid Strategies
- .Coello C.A.C. Evolutionary multiobjective optimization: A historical view of the field [J]. IEEE Computational Intelligence Magazine, 1; 2006.pp 28–36.
- .DEB, K.,A. PRATAP, S. AGARWAL, and T.MEYARIVAN. A fast and elitist multiobjective genetic algorithm: NSGA-II[C]. IEEE Transactions on Evolutionary Computation, 6, 2002a. pp:182–197.
- .Deb K, Goel T. Controlled elist non-dominated sorting genetic algorithms for better convergence [C]. 1st Int Conf on Evolutionary Multi-criterion Optimization. Zurich : Springer-Verlag , 2001 : 67-81.
- .SHEN Xiaoning, GUO Yu, CHEN Qingwei, HU Weili. Multi-objective optimization genetic algorithm keeping diversity of population[J]. Control and Decision.Vol. 23 No. 12, 2008 pp 1435-1440
- .Jinhua Zheng, et all. A Multi-objective Genetic Algorithm Based on Quick Sort[C], conference on artificial intelligence CAI 2004 Canada , pp:175-186
- .Zitzler E., M. Laumanns and L. Thiele. SPEA2: Improving the Strength ParetoEvolutionary Algorithm. Technical Report 103, Computer Engineering and Networks Laboratory (TIK), Swiss Federal Institute of Technology (ETH) Zurich, Gloriastrasse 35,CH-8092 Zurich, Switzerland. 2001
- .CORNE D. W., N. R. JERRAM, J. D. KNOWLES, and M. J. OATES. PESA-II: Region-based selection in evolutionary multiobjective optimization[C]. In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2001), Morgan Kaufmann Publishers, San Francisco, CA, 2001.pp 283–290.
- .SHI Ruifeng , ZHOU Hong , SHANG Guan Chunxia. A Hybrid Escalating Multi-objective Evolutionary Algorithm with Its Application to Flow Shop Problems [J]. Systems engineering-Theory & Practice 2006.8 pp101-109
- .BAI Zhijiang, LIU Guangzhong. Recursive multiple objective genetic algorithms. Journal of ShanghaiMaritime University[J]. 2007.6 pp62-67
- .GLOVER FW, LAGUNA M. Tabu search [M ]. Norwell, MA: Kluwer Academic Publishers, 1998.
- .Jon L. Bentley and Robert Sedgewick, Fast Algorithms for Sorting and Searching Strings, Proc. 8th Annual ACM-SIAM Symposium on Discrete Algorithms (SODA), 1997. pp 360-369,.
- .J. Schaffer, “Multiple objective optimization with vector evaluated genetic algorithms,” in First International Conference on Genetic Algorithms, J. Grefensttete, Ed., Hillsdale, NJ, 1987. pp 93–100.
- .E. Zitzler, K. Deb, and L. Thiele, “Comparison of multiobjective evolutionary algorithms: Empirical results,” Evolutionary Computation, vol. 8, no. 2, 2000. pp 173–195
- .Van Veldhuizen, D.A., and G.B. Lamont. On Measuring Multiobjective Evolutionary Algorithm Performance, 2000 IEEE Congress on Evolutionary Computation, 1, 2000. pp 204-211.
- .A. J. Nebro, E. Alba, and F. Luna, “Multi-objective optimization using grid computing,” Soft Computing, vol. 11, no. 6, 2007. pp 531–540
- .McGill R, Tukey JW, Larsen WA. Variations of boxplots. The American Statistician, 1978,32(1):12−16.
