article

Immune clonal coevolutionary algorithm for dynamic multiobjective optimization

Authors:

Yangyang LiAuthors Info & Claims

Natural Computing: an international journal, Volume 13, Issue 3

Pages 421 - 445

https://doi.org/10.1007/s11047-014-9415-z

Published: 01 September 2014 Publication History

Abstract

In this paper, a new evolutionary algorithm, called immune clonal coevolutionary algorithm (ICCoA) for dynamic multiobjective optimization (DMO) is proposed. On the basis of the basic principles of artificial immune system, the proposed algorithm adopts the immune clonal selection to solve DMO problems. In addition, the theory of coevolution is incorporated in ICCoA in global operation to preserve the diversity of Pareto-fronts. Moreover, coevolutionary competitive and cooperative operation is designed to enhance the uniformity and the diversity of the solutions. In comparison with NSGA-II, immune clonal algorithm for DMO and direction-based method, the simulation results obtained on 5 difficult test problems and on related performance metrics suggest that ICCoA can achieve better distributed solutions and be very effective in maintaining the uniformity of Pareto-fronts.

References

[1]

Abido MA (2010) Multiobjective particle swarm optimization with nondominated local and global sets. Nat Comput 9(1):747-766.

Digital Library

[2]

Cámara M, Ortega J, de Toro F (2009) Performance measures for dynamic multi-objective optimization. Bio-inspired systems: computational and ambient intelligence. Springer, Berlin, pp 760-767.

[3]

Chambers M, Cleveland WS, Kleiner B et al (1983) Graphical methods for data analysis. Wadsworth Brooks/Cole, Pacific Grover.

[4]

Chen WS, Jiao LC (2010) Adaptive tracking for periodically timevarying and nonlinearly parameterized systems using multilayer neural networks. IEEE Trans Neural Netw 21(2):345-351.

Digital Library

[5]

Coello Coello AC, Cortes NC (2002) An approach to solve multiobjective optimization problems based on an artificial immune system. in: Proceedings of 1st international conference artificial immune system, Int. Center Adv. Res. Identif. Sci. (ICARIS), pp 212-221.

[6]

de Castro LN, Timmis J (2002a) Artificial immune systems: a new computational intelligence approach. Springer, Berlin, pp 1-357.

[7]

de Castro LN, Timmis J (2002b) Learning and optimization using the clonal selection principle. IEEE Trans Evolut Comput 6(3): 239-251.

Digital Library

[8]

de Castro LN, Timmis J, Knidel H, Von Zuben F (2010) Artificial immune systems: structure, function, diversity and an application to biclustering. Nat Comput 9(3):575-577.

[9]

Deb K, Pratap A, Agarwal S, Meyarivan T (2002) A fast and elitist multi-objective genetic algorithm: NSGA-II. IEEE Trans Evolut Comput 6(2):182-197.

Digital Library

[10]

Farina M, Deb K, Amato P (2004) Dynamic multiobjective optimization problems: test cases, approximations, and applications. IEEE Trans Evolut Comput 8(5):425-442.

Digital Library

[11]

Forrest S, Perelson AS, Allen L, Cherukuri R (1994) Self-nonself discrimination in a computer. In: Proceedings of the 1994 IEEE symposium on research in security and privacy, IEEE Computer Society Press, Los Alamitos, 1994, pp 202-212.

[12]

Gibbons JD (1985) Nonparametric statistical inference, 2nd edn. Marcel Dekker, New York.

[13]

Goh CK, Tan KC (2007) An investigation on noisy environments in evolutionary multiobjective optimization. IEEE Trans Evolut Comput 11(3):354-381.

Digital Library

[14]

Goh CK, Tan KC (2009) A competitive-cooperative coevolutionary paradigm for dynamic multiobjective optimization. IEEE Trans Evolut Comput 13(1):103-127.

Digital Library

[15]

Gong MG, Du HF, Jiao LC (2006) Optimal approximation of linear systems by artificial immune response. Sci China Ser F 49(1): 63-79.

[16]

Gong MG, Jiao LC, Du HF, Bo LF (2008) Multiobjective immune algorithm with nondominated neighbor-based selection. Evolutionary computation. MIT Press, Cambridge, pp 225-255.

[17]

Helbig M, Engelbrecht AP et al (2013) Dynamic multi-objective optimization using PSO. Metaheuristics for Dynamic Optimization Studies in Computational Intelligence 433:147-188.

[18]

Huang L, Suh IH, Abraham A (2011) Dynamic multi-objective optimization based on membrane computing for control of timevarying unstable plants. Inf Sci 181(11):2370-2391.

Digital Library

[19]

Jiao LC, Wang L (2000) A novel genetic algorithm based on immunity. IEEE Trans Syst Man Cybern 30(5):552-561.

Digital Library

[20]

Jiao LC, Liu J, Zhong WC (2006) An organizational co-evolutionary algorithm for classification. IEEE Trans Evolut Comput 10(1):67-80.

Digital Library

[21]

Jin Y, Branke J (2005) Evolutionary optimization in uncertain environments--a survey. IEEE Trans Evolut Comput 9(3): 303-317.

Digital Library

[22]

Leung Y-W, Wang YP (2003) U-measure: a quality measure for multiobjective programming. IEEE Trans Syst Man Cybern 33(3):337-343.

Digital Library

[23]

Liu RC, Sheng ZC, Jiao LC, Zhang W (2010) Immunodomaince based clonal selection clustering algorithm. IEEE Congr Evolut Comput 2010:1-7.

Digital Library

[24]

Lung RI, Dumitrescu D (2010) Evolutionary swarm cooperative optimization in dynamic environments. Nat Comput 9(1):83-94.

Digital Library

[25]

Nusawardhana, Zak SH (2004) Simultaneous perturbation extremum seeking method for dynamic optimization problems. In: Proceedings of the 2004 American control conference. IEEE Press, Piseataway, 2004, pp 2805-2810.

[26]

Shang RH, Jiao LC, Gong MG, Lu B (2005) Clonal selection algorithm for dynamic muitiobjective optimization. In: Hao Y et al (Eds) Proceedings of the 2005 international conference on computational intelligence and security LNCS, vol 3801. Springer, Berlin, 2005, pp 846-851.

[27]

Shang RH, Jiao LC, Liu F, Ma WP (2012) A novel immune clonal algorithm for MO problems. IEEE Trans Evolut Comput 16(1):35-50.

Digital Library

[28]

Sun J, Fang W, Palade V, Wu XJ, Xu WB (2011) Quantum-behaved particle swarm optimization with Gaussian distributed local attractor point. Appl Math Comput 218:3763-3775.

[29]

Ursem RK, Krink T, Jensen MT, Michalewicz Z (2002) Analysis and modeling of control tasks in dynamic systems. IEEE Trans Evolut Comput 6(4):378-389.

Digital Library

[30]

Van Veldhuizen DA, Lamont GB (2000) On measuring multiobjective evolutionary algorithm performance. Congress on evolutionary computation (CEC 2000). IEEE Press, Piscataway, pp 204-211.

[31]

Wang YP, Dang CY (2008) An evolutionary algorithm for dynamic multi-objective optimization. Appl Math Comput 205(1):6-18.

[32]

Wang HF, Yang SX, Ip WH, Wang DW (2010) A particle swarm optimization based memetic algorithm for dynamic optimization problems. Nat Comput 9(3):703-725.

Digital Library

[33]

Woolley NC, Milanovic JV (2011) An immune system inspired clustering and classification method to detect critical areas in electrical power networks. Nat Comput 10(1):305-333.

Digital Library

[34]

Yang DD, Jiao LC, Gong MG (2009) Adaptive multiobjective optimization based on nondominated solutions. Comput Intell 25(2):84-108.

[35]

Zitzler E, Thiele L (2005) A simple mulf-membered evolution strategy to solve constraint optimization problems. IEEE Trans Evolut Comput 9(1):1-17.

Cited By

Gong DXu BZhang YGuo YYang S(2020)A Similarity-Based Cooperative Co-Evolutionary Algorithm for Dynamic Interval Multiobjective Optimization ProblemsIEEE Transactions on Evolutionary Computation10.1109/TEVC.2019.291220424:1(142-156)Online publication date: 28-Jan-2020
https://dl.acm.org/doi/10.1109/TEVC.2019.2912204
Li LLin QMing Z(2020)A Short Survey of Multi-objective Immune Algorithm Based on Clonal SelectionIntelligent Computing Theories and Application10.1007/978-3-030-60802-6_48(549-559)Online publication date: 2-Oct-2020
https://dl.acm.org/doi/10.1007/978-3-030-60802-6_48
Shang RDu BDai KJiao LXue Y(2018)Memetic algorithm based on extension step and statistical filtering for large-scale capacitated arc routing problemsNatural Computing: an international journal10.1007/s11047-016-9606-x17:2(375-391)Online publication date: 1-Jun-2018
https://dl.acm.org/doi/10.1007/s11047-016-9606-x
Show More Cited By

Immune clonal coevolutionary algorithm for dynamic multiobjective optimization
1. Information systems
  1. Data management systems
    1. Database administration
2. Theory of computation
  1. Design and analysis of algorithms

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cover image Natural Computing: an international journal

Natural Computing: an international journal Volume 13, Issue 3

September 2014

154 pages

ISSN:1567-7818

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Copyright © Copyright © 2014 Springer Science+Business Media Dordrecht.

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Kluwer Academic Publishers

United States

Publication History

Published: 01 September 2014

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Cited By

Gong DXu BZhang YGuo YYang S(2020)A Similarity-Based Cooperative Co-Evolutionary Algorithm for Dynamic Interval Multiobjective Optimization ProblemsIEEE Transactions on Evolutionary Computation10.1109/TEVC.2019.291220424:1(142-156)Online publication date: 28-Jan-2020
https://dl.acm.org/doi/10.1109/TEVC.2019.2912204
Li LLin QMing Z(2020)A Short Survey of Multi-objective Immune Algorithm Based on Clonal SelectionIntelligent Computing Theories and Application10.1007/978-3-030-60802-6_48(549-559)Online publication date: 2-Oct-2020
https://dl.acm.org/doi/10.1007/978-3-030-60802-6_48
Shang RDu BDai KJiao LXue Y(2018)Memetic algorithm based on extension step and statistical filtering for large-scale capacitated arc routing problemsNatural Computing: an international journal10.1007/s11047-016-9606-x17:2(375-391)Online publication date: 1-Jun-2018
https://dl.acm.org/doi/10.1007/s11047-016-9606-x
Qian SYe YJiang BXu G(2017)A Micro-cloning dynamic multiobjective algorithm with an adaptive change reaction strategySoft Computing - A Fusion of Foundations, Methodologies and Applications10.1007/s00500-016-2370-021:13(3781-3801)Online publication date: 1-Jul-2017
https://dl.acm.org/doi/10.1007/s00500-016-2370-0

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