Please wait a minute...
Frontiers of Computer Science

ISSN 2095-2228

ISSN 2095-2236(Online)

CN 10-1014/TP

Postal Subscription Code 80-970

2018 Impact Factor: 1.129

Front. Comput. Sci.    2014, Vol. 8 Issue (4) : 642-655    https://doi.org/10.1007/s11704-014-3093-y
RESEARCH ARTICLE
Reference direction based immune clone algorithm for many-objective optimization
Ruochen LIU(),Chenlin MA,Fei HE,Wenping MA,Licheng JIAO
Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, International Research Center for Intelligent Perception and Computation, Xidian University, Xi’an 710071, China
 Download: PDF(707 KB)  
 Export: BibTeX | EndNote | Reference Manager | ProCite | RefWorks
Abstract

In this paper, a new preference multi-objective optimization algorithm called immune clone algorithm based on reference direction method (RD-ICA) is proposed for solving many-objective optimization problems. First, an intelligent recombination operator, which performs well on the functions comprising many parameters, is introduced into an immune clone algorithm so as to explore the potentially excellent gene segments of all individuals in the antibody population. Second, a reference direction method, a very strict ranking based on the desire of decision makers (DMs), is used to guide selection and clone of the active population. Then a light beam search (LBS) is borrowed to pick out a small set of individuals filling the external population. The proposed method has been extensively compared with other recently proposed evolutionary multi-objective optimization (EMO) approaches over DTLZ problems with from 4 to 100 objectives. Experimental results indicate RD-ICA can achieve competitive results.

Keywords many-objective optimization      preference multiobjective optimization      artificial immune system      reference direction method      light beam search      intelligent recombination operator     
Corresponding Author(s): Ruochen LIU   
Issue Date: 11 August 2014
 Cite this article:   
Ruochen LIU,Chenlin MA,Fei HE, et al. Reference direction based immune clone algorithm for many-objective optimization[J]. Front. Comput. Sci., 2014, 8(4): 642-655.
 URL:  
https://academic.hep.com.cn/fcs/EN/10.1007/s11704-014-3093-y
https://academic.hep.com.cn/fcs/EN/Y2014/V8/I4/642
1 Farina M, Amato P. On the optimal solution definition for many-criteria optimization problems. In: Proceedings of International Conference of the NAFIPS-FLINT. 2002, 233-238
2 Freschi F, Repetto M. Multiobjective optimization by a modified artificial immune system algorithm. In: Proceedings of the 4th International Conference on Artificial Immune Systems. 2005, 3627: 248-261
doi: 10.1007/11536444_19
3 Rachmawati L, Srinivasan D. Preference incorporation in multiobjective evolutionary algorithms: a survey. In: Proceedings of 2006 IEEE Congress on Evolutionary Computation. 2006, 3385-3391
doi: 10.1109/CEC.2006.1688414
4 Yang D D, Jiao L C, Gong M G, Feng J. Adaptive ranks clone and k-nearest neighbor list-based immune multi-objective optimization. Computational Intelligence, 2010, 26(4): 359-385
doi: 10.1111/j.1467-8640.2010.00363.x
5 Deb K, Kummar A. Light beam search based multi-objective optimization using evolutionary algorithms. In: Proceedings of the 2007 IEEE Congress on Evolutionary Computation. 2007, 2125-2132
doi: 10.1109/CEC.2007.4424735
6 Zitzler E, Deb K, Thiele L. Comparison of multiobjective evolutionary algorithms: empirical results. Evolutionary Computation, 2000, 8(2): 173-195
doi: 10.1162/106365600568202
7 Deb K, Thiele L, Laumanns M, Zitzler E. Scalable test problems for evolutionary multi-objective optimization. In: Abraham A, Jain L and Goldberg R, eds. Evolutionary multiobjective optimization, Springer London, 2005, 105-145
doi: 10.1007/1-84628-137-7_6
8 Gong M G, Jiao L C, Du H F, Bo L F. Multiobjective immune algorithm with nondominated neighbor-based selection. Evolutionary Computation, 2008, 16(2): 225-255
doi: 10.1162/evco.2008.16.2.225
9 Deb K, Pratap A, Agarwal S, Meyarivan T. A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation, 2002, 6(2): 182-197
doi: 10.1109/4235.996017
10 Zitzler E, Laumanns M, Thiele L. SPEA2: improving the performance of the strength pareto evolutionary algorithm. Technical Report 103, Computer Engineering and Communication Networks Lab (TIK), Swiss Federal Institute of Technology (ETH) Zurich, 2001
11 Corne D W, Knowles J D, Oates M J. The Pareto-envelope based selection algorithm for multi-objective optimization. In: Proceedings of the Parallel Problem Solving from Nature VI Conference. 2000, 839-848
doi: 10.1007/3-540-45356-3_82
12 Coello Coello C A, Cortes N C. Solving multiobjective optimization problems using an artificial immune system. Genetic Programming and Evolvable Machines, 2005, 6(2): 163-190
doi: 10.1007/s10710-005-6164-x
13 Jiao L C, Gong M G, Shang R H, Du H F, Lu B. Clonal selection with immune dominance and energy based multi-objective optimization. In: Proceeding of the 3rd International Conference on Evolutionary Multicriterion Optimization, EMO. 2005, 474-489
14 Freschi F, Repetto M. VIS: an artificial immune network for multiobjective optimization. Engineering Optimization, 2006, 38(8): 975-996
doi: 10.1080/03052150600880706
15 Garza-Fabre M, Pulido G T, Coello Coello C A. Ranking methods for many-objective optimization. Lecture Notes in Computer Science, 2009, 5845: 633-645
doi: 10.1007/978-3-642-05258-3_56
16 di Pierro F. Many-objective evolutionary algorithms and applications to water resources engineering. PhD thesis, University of Exeter, UK, <month>August</month>2006
17 di Pierro F, Djordjevic S, Khu S, Savic D, Walters G A. Automatic calibration of urban drainage model using a novel multi-objective GA. Water Science and Technology, 2005, 52(5): 43-52
18 Garcia S, Molina D, Lozano M, Herrera F. A study on the use of nonparametric tests for analyzing the evolutionary algorithms’ behavior: a case study on the CEC’2005 special session on real parameter optimization. Journal of Heuristics, 2005, 15: 617-644
19 Praditwong K, Yao X. A new multi-objective evolutionary optimization algorithm: the two-archive algorithm. In: Proceeding of International Conference on Computational Intelligence and Security. 2006, 1: 286-291
20 Fonseca C M, Fleming P J. Genetic algorithms for multiobjective optimization: formulation, discussion and generalization. In: Proceedings of the 5th International Conference on Genetic Algorithms. 1993, 416-423
21 Deb K, Sundar J, Uday Bhaskara Rao N, Chandhuri S. Reference point based multi-objective optimization using evolutionary algorithms. International Journal of Computational Intelligence Research, 2006, 2(3): 273-286
doi: 10.5019/j.ijcir.2006.67
22 Jaszkiewicz A, Slowinski R. The light beam search approach–an overview of methodology and applications. European Journal of Operation Research, 1999, 113(2): 300-314
doi: 10.1016/S0377-2217(98)00218-5
23 Thiele L, Miettinen K, Korhonen P J, Molina J. A preference-based evolutionary algorithm for multi-objective optimization. Evolutionary Computation, 2009, 17(3): 411-436
doi: 10.1162/evco.2009.17.3.411
24 Molina J, Santana L V, Hernandez-Diaz A G, Coello Coello C A, Caballero R. g-dominance: reference point based dominance for multiobjective metaheuristics. European Journal of Operational Research, 2009, 197(2): 685-692
doi: 10.1016/j.ejor.2008.07.015
25 Lamjed B S, Slim B, Khaled G. The r-dominance: a new dominance relation for interactive evolutionary multicriteria decision making. IEEE Transactions on Evolutionary Computation, 2010, 14(5): 801-818
doi: 10.1109/TEVC.2010.2041060
26 Laumanns M, Thiele L, Deb K. Combining convergence and diversity in evolutionary multiobjective optimization. Evolutionary Computation, 2002, 10(3): 263-282
doi: 10.1162/106365602760234108
27 Deb K, Mohan M, Mishra S. Toward a quick computation of wellspread Pareto-optimal solutions. In: Proceedings of the 2nd International Conference of Evolutionary Multi-criterion Optimization. 2003, 2632: 222-236
doi: 10.1007/3-540-36970-8_16
28 Farina M, Amato P. On the optimal solution definition for many-criteria optimization problems. In: Proceedings of International Conference of the NAFIPS-FLINT. 2002, 233-238
29 Park S H. Robust design and analysis for quality engineering. Taylor & Francis, 1998, 40(4): 348-349
30 Leung Y W, Wang Y. An orthogonal genetic algorithm with quantization for global numerical optimization. IEEE Transactions on Evolutionary Computation, 2001, 5(1): 41-53
doi: 10.1109/4235.910464
31 Ho S, Shu L, Chen J. Intelligent evolutionary algorithms for large parameter. IEEE Transactions on Evolutionary Computation, 2004, 8(6): 522-541
doi: 10.1109/TEVC.2004.835176
32 de Castro L N, Von Zuben F J. Learning and optimization using the clonal selection principle. IEEE Transactions on Evolutionary Computation, Special Issue on Artificial Immune Systems, 2002, 6(3): 239-251
33 Cutello V, Nicosia G, Pavone M. Exploring the capability of immune algorithms: a characterization of hypermutation operators. In: Proceedings of 3rd International Conference on Artificial Immune Systems. 2004, 3239: 263-276
doi: 10.1007/978-3-540-30220-9_22
34 Deb K, Kumar A. Interactive evolutionary multi-objective optimization and decision-making using reference direction method. In: Proceeding of the 9th Annual Conference on Genetic and Evolutionary Computation (GECCO’07). 2007, 781-788
doi: 10.1145/1276958.1277116
35 Van Veldhuizen D A. Multi-objective evolutionary algorithms: classification, analyzes, and new innovations. PhD thesis, Wright-Patterson AFB: Air Force Institute of Technology, <month>June</month>1999
36 Schott J R. Fault tolerant design using single and multicriteria genetic algorithm optimization. Master’s thesis, Massachusetts Institute of Technology, <month>May</month><?Pub Caret?>1995
[1] Shaha AL-OTAIBI, Mourad YKHLEF. Hybrid immunizing solution for job recommender system[J]. Front. Comput. Sci., 2017, 11(3): 511-527.
[2] Ruochen LIU, Licheng JIAO, Yangyang LI, Jing LIU, . An immune memory clonal algorithm for numerical and combinatorial optimization[J]. Front. Comput. Sci., 2010, 4(4): 536-559.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed