Skip to main content

Advertisement

SpringerLink
Log in

A novel cooperative coevolutionary dynamic multi-objective optimization algorithm using a new predictive model

  • Methodologies and Application
  • Published:
Soft Computing Aims and scope Submit manuscript

Abstract

Dynamic multi-objective optimization problem (DMOP) is quite challenging and it dues to that there are multiple conflicting objects changing over with time or environment. In this paper, a novel cooperative coevolutionary dynamic multi-objective optimization algorithm (PNSCCDMO) is proposed. The main idea of a new cooperative coevolution based on non-dominated sorting is that it allows the decomposition process of the optimization problem according to the search space of decision variables, and each species subcomponents will cooperate to evolve for better solutions. This way derives from nature and can improve convergence significantly. A modified linear regression prediction strategy is used to make rapid response to the new changes in the environment. The effectiveness of PNSCCDMO is validated against various of DMOPs compared with the other four algorithms, and the experimental result indicates PNSCCDMO has a good capability to track the Pareto front as it is changed with time in dynamic environments.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

References

  • Anderson TW, Scolve S (1978) Introduction to the statistical analysis of data. Houghton Mifflin, Boston

    MATH  Google Scholar 

  • Bader J, Zitzler E (2008) HypE: an algorithm for fast hypervolume-based manyobjective optimization. Tech. Rep. TIK 286, Computer Engineering and Networks Laboratory, ETH Zurich

  • Bader J, Zitzler E (2010) Robustness in hypervolume-based multiobjective search. Tech. Rep. TIK 317, Computer Engineering and Networks Laboratory, ETH Zurich

  • Bendtsen CN, Krink T (2002) Dynamic memory model for non-stationary optimization. In: Proceedings of the 2002 congress on evolutionary computation, vol 1, issue 12–17, Piscataway, IEEE Press, pp 992–997

  • Brockhoff D, Zitzler E (2007) Improving hypervolume-based multiobjective evolutionary algorithms by using objective reduction methods. In: IEEE congress on evolutionary computation, pp 2086–2093

  • Coello CAC, Sierra MR (2003) A coevolutionary multi-objective evolutionary algorithm. In: IEEE of proceeding congress on, evolutionary computation, vol 1, pp 482–489

  • Deb K, Pratap A, Agarwal S, Meyarivan T (2002) A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans Evol Comput 6:182–197

    Article  Google Scholar 

  • Deb K, Bhaskara UN, Karthik S (2007) Dynamic multi-Objective optimization and decision-making using modified NSGA-II: a case study on hydro-thermal power scheduling. In: Fourth international conference on evolutionary multi-criterion optimization. LNCS, vol 4403, Matsushima, Springer, pp 803–807

  • Deb K, Kumar A (2007) Interactive evolutionary multi-objective optimization and decision-making using reference direction method. Tech. Rep., Indian Institute of Technology, KanGAL 2007001

  • Deb K, Sinha A, Korhonen P et al (2009) An interactive evolutionary multiobjective optimization method based on progressively approximated value functions. Tech. Rep. KanGAL 2009005, Indian Institute of Technology

  • Elhossini A, Areibi S, Dony R (2010) Strength Pareto particle swarm optimization and hybrid EA-PSO for multi-objective optimization. Evol Comput 18(1):127–156

    Article  Google Scholar 

  • Eriksson R, Olsson B, (2004) On the performance of evolutionary algorithms with lifetime adaptation in dynamic fitness landscapes. In: Proceeding of 2004 congress on evolutionary computing. Piscataway, IEEE Press, pp 1293–1300

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

    Article  Google Scholar 

  • Farina M, Zhang QF, Jin YC et al (2005) Linked interpolation–optimization strategies for multicriteria optimization problems. Soft Comput 9(1):54–65

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Gong MG, Jiao LC, Du HF et al (2008) Multiobjective immune algorithm with nondominated neighbor-based selection. Evol Comput 16(2):225–255

    Article  Google Scholar 

  • Grefenstette JJ (1992) Genetic algorithms for changing environments. Parallel problem solving from nature, Brussels, pp 137–144

  • Hadad BS, Eick CF (1997) Supporting polyploidy in genetic algorithms using dominance vectors. In: Proceedings of the 6th international conference on evolutionary programming, Morgan Kaufmann, San Francisco, pp 223–234

  • Hughes EJ (2003) Multiple single objective Pareto sampling. In: Proceedings of the congress on evolutionary computation, Canberra, IEEE Press, pp 2678–2684

  • Iason H, David W (2006) Dynamic multiobjective optimization with evolutionary algorithms: a forward-looking approach. In: Proceeding of the GECCO’ 06, Washington, USA, pp 1201–1208

  • Jin YC, Sendhoff B (2004) Constructing dynamic optimization test problems using the multiobjective optimization concept. Evolutionary computing. Lecture notes in computer science, vol 3005, Springer, pp 525–536

  • Keerativuttiumrong N, Chaiyaratana N, Varavithya V (2002) Multiobjective co-operative co-evolutionary genetic algorithm. Lecture notes in computer science, vol 2439, Springer, pp 288–297

  • Lewis J, Hart E, Ritchie G (1998) A comparison of dominance mechanism and simple mutation on non-stationary problems. Parallel problem solving from nature. LNCS, vol 1498, pp 139–148

  • Li BB, Wang L (2007) A hybrid quantum-inspired genetic algorithm for multiobjective flow shop scheduling. IEEE Trans Syst Man Cybern Part B (Cybernetics) 37(3): 576–591

    Google Scholar 

  • Lohn JD, Kraus WF, Haith GL (2002) Comparing a coevolutionary genetic algorithm for multiobjective optimization. In: Proceedings of the 2002 IEEE congress on evolutionary computation, pp 1157–1162

  • Ma YJ, Liu RC, Shang RH (2011) A hybrid dynamic multi-objective immune optimization algorithm using prediction strategy and improved differential evolution crossover operator. Neural information processing. Lecture notes in computer science, vol 7063, Springer, pp 435–444

  • Maneeratana K, Boonlong K, Chaiyaratana N (2004) Multi-objective optimisation by co-operative co-evolution. Lecture notes in computer science, vol 3242, Springer, pp 772–781

  • Mehnen J, Tobias W, Gnter R (2006) Evolutionary optimization of dynamic multiobjective functions. Technical Report CI-204/06, University of Dortmund

  • Mori N, Kita H, Nishikawa Y (1998) Adaptation to a changing environment by means of the dynamical genetic algorithm. Lecture notes in computer science, vol 1498. Springer, pp 149–158

  • Morrison RW, Jong KA (2000) Triggered hyper-mutation revisited. In: Proceeding of congress on evolutionary computation, Piscataway, IEEE Press, pp 1025–1032

  • Oppacher F, Wineberg M (1999) The shifting balance genetic algorithm: improving the GA in a dynamic environment. In: Proceeding of genetic and evolutionary computation, Morgan Kaufmann, San Francisco, pp 504–510

  • Potter MA, De Jong KA (2000) Cooperative coevolution: an architecture for evolving coadapted subcomponents. Evol Comput 8(1):1–29

    Google Scholar 

  • Ryan C (1997) Diploid without dominance. In: Proceeding of the 3th Nordic workshop on genetic algorithms, pp 63–70

  • Ryan C, Collins JJ (1997) Polygenic inheritance—a haploid scheme that can outperform diploidy. In: Proceeding of the 5th international conference on parallel problem solving from nature, Springer, Berlin, pp 178–187

  • Schott JR (1995) Fault tolerant design using single and multictiteria genetic algorithm optimization. Master thesis, Massachusetts Institute of Technology

  • Shang RH, Jiao LC, Gong MG et al (2005) Clonal selection algorithm for dynamic multiobjective optimization. Computational intelligence and security. Lecture notes in computer science, vol 3801, Springer, Berlin, pp 846–851

  • Tan KC, Yang YJ, Goh CK (2006) A distributed cooperative coevolutionary algorithm for multiobjective optimization. IEEE Trans Evol Comput 10(5):527–549

    Google Scholar 

  • Teich J (2001) Pareto-front exploration with uncertain objectives. Lecture notes in computer science, vol 1993. Springer, pp 314–328

  • Thiele L, Miettinen K, Korhonen PJ et al (2009) A preference-based evolutionary algorithm for multi-objective optimization. Evol Comput 17(3):411–436

    Article  Google Scholar 

  • Ursem RK (2000) Multinational GA optimization technique in dynamic environments. In: Proceeding of genetic and evolutionary computation, Morgan Kaufmann, San Francisco, pp 19–26

  • Van Veldhuizen, David A (1999) Multi-objective evolutionary algorithms: classification, analyzes, and new innovations. PhD thesis, Wright-Patterson AFB, Air Force Institute of Technology

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

    Article  MathSciNet  Google Scholar 

  • Zeng SY, Chen G, Zhang LF et al (2002) A dynamic multi-objective evolutionary algorithm based on an orthogonal design. In: Proceeding of the congress on evolutionary computation, Vancouver, BC, Canada, IEEE Press, pp 2588–2595

  • Zeng SY, Yao SZ, Kang LS et al (2005) An efficient multi-objective evolutionary algorithm: OMOEA-II. In: Third international conference on evolutionary multi-criterion optimization. INCS, vol 3401, Springer, pp 108–119

  • Zhang QF, Li H (2007) MOEA/D: a multiobjective evolutionary algorithm based on decomposition. IEEE Trans Evol Comput 11(6):712–731

    Article  Google Scholar 

  • Zhang ZH (2008) Multiobjective optimization immune algorithm in dynamic environments and its application to greenhouse control. Appl Soft Comput 8:959–971

    Article  Google Scholar 

  • Zhang ZH, Qian SQ (2011) Artificial immune system in dynamic environments solving time-varying non-linear constrained multi-objective problems. Soft Comput 15:1333–1349

    Article  Google Scholar 

  • Zhou AM, Jin YC, Zhang QF et al (2007) Prediction-based population re-initialization for evolutionary dynamic multi-objective optimization. In: Conference on evolutionary multi-criterion optimization. Lecture notes in computer science, vol 4403, Springer, Berlin, pp 832–846

  • Zhou AM, Zhang QF, Jin YC et al. (2006) Modeling the population distribution in multi-objective optimization by generative topographic mapping. Lecture notes in computer science, vol 4193, Springer, pp 443–452

  • Zhou AM, Qu BY, Li H et al (2011) Multiobjective evolutionary algorithms: a survey of the state of the art. Swarm Evol Comput 1:32–49

    Article  Google Scholar 

  • Zitzler E, Laumanns M, Thiele L (2002) SPEA2: improving the strength Pareto evolutionary algorithm for multiobjective optimization. In: Proceeding Of the evolutionary methods for design, optimization and control, Barcelona, Spain, pp 95–100

Download references

Acknowledgments

The authors would like to thank the editor and the reviewers for helpful comments that greatly improved the paper. This work was supported by the National Natural Science Foundation of China (Nos. 61373111, 61272279, 61103119 and 61203303); the Fundamental Research Funds for the Central University (Nos. K50511020014, K5051302084, K50510020011, K5051302049, and K5051302023); the Fund for Foreign Scholars in University Research and Teaching Programs (the 111 Project) (No. B07048); and the Program for Century Excellent Talents in University (No. NCET-12-0920).

Author information

Authors and Affiliations

  1. Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China, Xidian University, Xi’an , 710071, China

    Ruochen Liu, Yangyang Chen, Wenping Ma, Caihong Mu & Licheng Jiao

Authors
  1. Ruochen Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yangyang Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Wenping Ma
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Caihong Mu
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Licheng Jiao
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ruochen Liu.

Additional information

Communicated by E. Munoz.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Liu, R., Chen, Y., Ma, W. et al. A novel cooperative coevolutionary dynamic multi-objective optimization algorithm using a new predictive model. Soft Comput 18, 1913–1929 (2014). https://doi.org/10.1007/s00500-013-1175-7

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00500-013-1175-7

Keywords

Search

Navigation