高维多目标问题的排序新方法

doi:10.3969/j.issn.1001-2400.2014.06.015

Abstract

Abstract:

A new ranking method is proposed to solve many-objective optimization problems. It can be used to generates many approximate optimal objective vectors to increase the size of population, so that the individuals can be efficiently sorted by using non-dominance sorting. An ideal Pareto Front is first constructed, and then the ideal Pareto Front is divided into a number of grids. For each individual, it is uniquely assigned to a grid, and then some nodes of the grid are used to determine whether the individual is a non-dominance solution. Experimental results show that, even for 50-objective optimization problems, the convergence measurements of solutions obtained by the ranking method are smaller than 1. Meanwhile, compared with two state-of-the-art relaxed forms of Pareto dominance, the experimental results show that this ranking method can simultaneously maintain the diversity of solutions and have good convergence.

Key words: many-objective optimization, evolutionary algorithms, ranking, Pareto optimisation

CLC Number:

DAI Cai;WANG Yuping;HE Xiaoguang. New ranking method for many-objective problems[J].J4, 2014, 41(6): 89-94.

References

［1］ Kokshenev I, Braga A P. An Efficient Multi-objective Learning Algorithm for RBF Neural Network ［J］. Neurocomputing, 2010, 73(16-18): 2799-2808.
［2］ Depolli M, Trobec R, Filipc B. Asynchronous Master-Slave Parallelization of Differential Evolution for Multi-objective Optimization ［J］. Evolutionary Computation, 2013, 21(2): 261-291.
［3］ Liao H L, Wu Q H. Multi-objective Optimization by Learning Automata ［J］. Journal of Global Optimization, 2013, 55(2): 459-487.
［4］ Ishibuchi H, Tsukamoto N, Nojima Y. Evolutionary Many-objective Optimization: a Short Review ［C］//IEEE Congress on Evolutionary Computation. Piscataway: IEEE, 2008: 2419-2426.
［5］ Bader J, Zitzler E. HypE: an Algorithm for Fast Hypervolume-based Many-objective Optimization ［J］. Evolutionary Computation, 2011, 19(1): 45-76.
［6］ Friedrich T, Horoba C, Neumann, F. Multiplicative Approximations and the Hypervolume Indicator ［C］//Proceedings of the 11th Annual Genetic and Evolutionary Computation Conference. New York: ACM, 2009: 571-578.
［7］ Zitzler E, Künzli S. Indicator-based Selection in Multiobjective Search ［C］//Lecture Notes in Computer Science: 3242. Berlin: Springer, 2004: 832-842.
［8］ Ray T, Asafuddoula M D, Isaacs A. A Steady State Decomposition Based Quantum Algorithm for Many Objective Optimization ［C］//IEEE Congress on Evolutionary Computation. Piscataway: IEEE, 2013: 2817-2824.
［9］ Kokolo I, Hajime K, Shigenobu K. Failure of Pareto-based MOEAs: Does Nondominated Really Mean Near to Optimal? ［C］//Proceedings of Congress on Evolutionary Computation. Piscataway: IEEE, 2001: 957-962.
［10］马晶晶, 杨咚咚, 焦李成. 免疫非支配自适应粒子群多目标优化［J］. 西安电子科技大学学报, 2010, 37(5): 846-851.
Ma Jingjing, Yang Dongdong, Jiao Licheng. Immune Nondominated Adaptive Particle Swarm Multi-objective Optimization ［J］. Journal of Xidian University, 2010, 37(5): 846-851.
［11］ Sato H, Aguirre H E, Tanaka K. Controlling Dominance Area of Solutions and Its Impact on the Performance of mOEAs ［C］//Lecture Notes in Computer Science: 4003. Heidelberg: Springer, 2007: 5-20.
［12］ Deb K. Multi-objective Optimization Using Evolutionary Algorithms ［M］. New Jersey: John Wiley ＆ Sons, 2001.
［13］ Deb K, Agrawal S, Pratap A, et al. A Fast and Elitist Multiobjective Genetic Algorithm: NSGA-Ⅱ ［J］. IEEE Transactions on Evolutionary Computation, 2002, 6(2):182-197.
［14］ Coello C A, Cortés N C. Solving Multiobjective Optimization Problems Using an Artificial Immune System ［J］. Genetic Programming and Evolvable Machines, 2005, 6(2): 163-190.
［15］ Deb K, Thiele L, Laumanns M, et al. Scalable Multi-objective Optimization Test Problems ［C］//Proceedings of Congress on Evolutionary Computation. Piscataway: IEEE, 2002: 825-830.
［16］ Tan Yanyan, Jiao Yongchang, Li Hong, et al. MOEA/D+uniform Design: a New Version of MOEA/D for Optimization Problems with Manyobjectives ［J］. Computers ＆ Operations Research, 2013, 40(6):1648-1660.

[1]	YANG Wusi,CHEN Li,WANG Yi,ZHANG Maosheng. Many-objective particle swarm optimization algorithm for fitness ranking [J]. Journal of Xidian University, 2021, 48(3): 78-84.
[2]	LIU Mingqian,MENG Yan,ZHANG Weidong. Method for comprehensive evaluation of effectiveness of radar emitter signals recognition [J]. Journal of Xidian University, 2019, 46(6): 1-8.
[3]	WEN Honghang;GE Jianhua;XU Tangwen. Low complexity method for PAPR reduction of OFDM signals [J]. J4, 2015, 42(5): 188-193.
[4]	CHENG Wei;SHI Haoshan;LI Dong. Novel localization algorithm based on evolutionary programming resampling in WSN [J]. J4, 2011, 38(4): 154-159.
[5]	MU Cai-hong;JIAO Li-cheng;LIU Yi. M-Elite coevolutionary algorithm for constrained optimization [J]. J4, 2010, 37(5): 852-861.
[6]	LU Wei;GUAN Lin-tao. Search model of P2P bases on tactics of ranking in group [J]. J4, 2007, 34(3): 486-489.
[7]	SUN De-hua(1);SHI Jia-rong(2);LIU San-yang(1). An application of random simulations in multiple attribute decision making with incomplete information [J]. J4, 2006, 33(2): 287-291.
[8]	LIU Ruo-chen1;2;DU Hai-feng1;JIAO Li-cheng1. Immune monoclonal strategy based on the Cauthy mutation [J]. J4, 2004, 31(4): 551-556.

New ranking method for many-objective problems

PDF (PC)

Like

Knowledge

Abstract

Cite this article

share this article

References

Related Articles 8

Metrics

Comments

Recommended 0