Abstract
We try to improve the NSGA-II, one of the most classical MOP algorithms, in two ways. To measure individual crowding distance by edge weight of minimum spanning tree and k-nearest-neighbors Pareto rank assignment strategy is helpful on diversity of population; A compound crossover operator increases the extent and the ability of search. Experimental results on ZDTs and DTLZs, suggest that A K-Nearest-Neighbors Pareto Rank Assignment Strategy and Compound Crossover Operator Based NSGA-II (KC NSGA-II) works faster and has more diverse solutions than its origins.
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References
Srinivas, N., Deb, K.: Multi-objective optimization using non-dominated sorting ingenetic algorithms. Evolutionary Computation 2(3), 221–248 (1994)
Srinivas, N., Deb, K.: Multi-Objective Function Optimization Using Non-Dominated Sorting Genetical Algorithms. Evolutionary Computation 2(3), 221–248 (1995)
Deb, K.: Multi-objective genetic algorithms: Problem difficulties and construction of test functions. Evolutionary Computation 7(3), 205 (1999)
Mitra, K.: Multi-objective optimization of an industrial grinding operation using elitist nondominated sorting genetic algorithm. Chemical Engineering Science, 385–396 (2004)
Parsopoulos, K.E., Tasoulis, D.K., Pavlidis, N.G., Plagianakos, V.P., Vrahatis, M.N.: Vector Evaluated Differential Evolution for – Multiobjective Optimization
Liu, Y.: A fast optimization method of using nondominated sorting genetic algorithm (NSA-II) and 1-nearest neighbor (1NN) classifier for numerical model calibration. In: IEEE International Conference on 2005, pp. 544–549 (2005)
Akbari, A.A.: Pareto-Optimal Solutions for Multi-Objective Optimization of Turning Operation using Non-dominated Sorting Genetic Algorithm. In: TICME 2005, pp. 404–413 (2005)
Wei, X.: Ramp Shape Optimum Design for Airplane Land-Based Ski-Jump Take off via NSGA II. In: International Conference on 2006, pp. 995–1000 (2006)
Price, A.R.: Mul-tiobjective Tuning of Grid-Enabled Earth System Models Using a Non-dominated Sorting Genetic Algorithm (NSGA-II). In: IEEE International Conference on 2006, p. 117 (2006)
Nazemi, A.: Extracting a Set of Robust Pareto-Optimal Parameters for Hydrologic Models using NSGA-II and SCEM. In: IEEE Congress on 2006, pp. 1901–1908 (2006)
Deb, K., Thiele, L., Laumanns, M., Zitzler, E.: Scalable multi-objective optimization test proiproblems. In: Congress on Evolutionary Computation, pp. 825–830 (2002)
Shi, Y.: A New Strategy for Parameter Estimation of Dynamic Differential Equations Based on NSGA II. Simulated evolution and learning, 345–352 (2006)
Bharti, S.: Optimal structural design of a morphing aircraft wing using parallel non-dominated sorting genetic algorithm II (NSGA II). Smart structures and materials, 616602-1–616602-12 (2006)
Xu, L.: Multi-objective Parameters Selection for SVM Classification Using NSGA-II. Advances in data mining, 365–376 (2006)
Goudos, S.K.: Electric Filter Optimal Design Suitable For Microwave Communications By Multi-Objective Evolutionary Algorithms. Microwave and Optical Technology Letters, 2324–2329 (2007)
Gallego, R.A., Monticelli, A., Romero, R.: Transmission system expansion planning by an extended genetic algorithm. IEE Proc. Gener,Transm. Distrib. 145(3), 329–335 (1998)
Guo, T., Kang, L.: A New Algorithm to Optimize Function with In equation and Restraints. Journal of Wuhan University: Natural Science 45(5), 771–775 (1999)
Huang, Z.: A Effective Multi-Objective Evolutionary Algorithm. Computer Engineering and Applications 43(11), 75–79 (2007)
Yu, R., Wang, Y.: A NSGA-II for Multi-Objective Minimum Spanning Tree Problem. Electronic Sci. & Tech. 126(6), 33–36 (2007)
Yang, S.: A Multi-Objective Evolutionary Algorithm Based on Parato Optimility and Limited Elitist. Computer Engineering and Applications 43(2), 108–111 (2007)
Peng, W., Huang, H.: A novel Multi-Objective Immune Algorithm Based on Memory Clonal Selection. Computer Engineering and Applications 44(16), 56–59 (2008)
Li, M., Zheng, J.: Mutil-objective evolutionary algorithm based on neighborhood. Computer Application 28(6), 1571–1574 (2008)
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Guo, W., Li, Z., Zhao, D., Wong, T. (2008). A K-Nearest-Neighbors Pareto Rank Assignment Strategy and Compound Crossover Operator Based NSGA-II and Its Applications on Multi-objective Optimization Functions. In: Kang, L., Cai, Z., Yan, X., Liu, Y. (eds) Advances in Computation and Intelligence. ISICA 2008. Lecture Notes in Computer Science, vol 5370. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-92137-0_16
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DOI: https://doi.org/10.1007/978-3-540-92137-0_16
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