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A dynamic multi-objective evolutionary algorithm using a change severity-based adaptive population management strategy

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Abstract

In addition to the need for simultaneously optimizing several competing objectives, many real-world problems are also dynamic in nature. These problems are called dynamic multi-objective optimization problems. Applying evolutionary algorithms to solve dynamic optimization problems has obtained great attention among many researchers. However, most of works are restricted to the single-objective case. In this work, we propose an adaptive hybrid population management strategy using memory, local search and random strategies, to effectively handle environment dynamicity for the multi-objective case where objective functions change over time. Moreover, the proposed strategy is based on a new technique that detects the change severity, according to which it adjusts the number of memory and random solutions to be used. This ensures, on the one hand, a high level of convergence and on the other hand, the required diversity. We propose a dynamic version of the Non dominated Sorting Genetic Algorithm II, within which we integrate the above-mentioned strategies. Empirical results show that our proposal based on the use of the adaptive strategy is able to handle dynamic environments and to track the Pareto front as it changes over time. Moreover, when confronted with several recently proposed dynamic algorithms, it has presented competitive and better results on most problems.

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References

  • Amato P, Farina M (2005) An alife-inspired evolutionary algorithm for dynamic multi-objective optimization problems. Adv Soft Comput 32:113–125

  • Azzouz R, Bechikh S, Ben Said L (2015) Multi-objective optimization with dynamic constraints and objectives: new challenges for evolutionary algorithms. In: Genetic and evolutionary computation conference (GECCO 2015)

  • Azzouz R, Bechikh S, Said LB (2014) A multiple reference point-based evolutionary algorithm for dynamic multi-objective optimization with undetectable changes. In: Proceedings of the IEEE congress on evolutionary computation, pp 3168–3175

  • Blackwell T, Branke J (2006) Multiswarms, exclusion, and anti-convergence in dynamic environments. IEEE Trans Evolut Comput 10(4):459–472

    Article  Google Scholar 

  • Bosman PAN (2007) Learning and anticipation in online dynamic optimization. In: Evolutionary computation in dynamic and uncertain environments, pp 129–152

  • Cámara M, Ortega J, de Toro F (2007) Parallel processing for multi-objective optimization in dynamic environments. In: Proceedings of the IEEE international parallel and distributed processing symposium, pp 1–8

  • Cámara M, Ortega J, de Toro F (2008) Parallel multi-objective optimization evolutionary algorithms in dynamic environments. In: Proceedings of the first international workshop on parallel architectures and bioinspired algorithms, vol 1, pp 13–20

  • Cedeno W, Vemuri VR (1997) On the use of niching for dynamic landscapes. In: Proceedings of the international conference on evolutionary computation, pp 361–366

  • Cobb HG (1990) An investigation into the use of hypermutation as an adaptive operator in genetic algorithms having continuous, time-dependent nonstationary environments, Tech. Rep. AIC-90-001, Naval Research Laboratory

  • Deb K (2011) Single and multi-objective dynamic optimization: two tales from an evolutionary perspective. Tech. Rep. 2011004, Kanpur Genetic Algorithms Laboratory

  • Deb K, Agrawal S, Pratap A, Meyarivan T (2000) A fast elitist non-dominated sorting genetic algorithm for multi-objective optimization: Nsga-ii. In: Proceedings of the 6th international conference on parallel problem solving from nature, vol 1917, pp 849–858

  • Deb K, Rao U, Karthik S (2007) Dynamic multi-objective optimization and decision-making using modified nsga-ii: a case study on hydro-thermal power scheduling. In: Proceedings of the 4th international conference, EMO 2007, vol 4403, pp 803–817

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

    Article  Google Scholar 

  • Fogel LJ, Owens AJ, Walsh MJ (1966) Artificial intelligence through simulated evolution

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

    Article  Google Scholar 

  • Grefenstette JJ (1992) Genetic algorithms for changing environments. In: Proceedings of the second international conference on parallel problem solving from nature, pp 137–144

  • Guan SU, Chen Q, Mo W (2005) Evolving dynamic multi-objective optimization problems with objective replacement. Artif Intell Rev 23(3):267–293

    Article  Google Scholar 

  • Hatzakis I, Wallace D (2006) Dynamic multi-objective optimization with evolutionary algorithms: a foward-looking approach. In: Proceedings of the 2006 genetic and evolutionary computation conference, pp 1201–1208

  • Helbig M, Engelbrecht AP (2014) Benchmarks for dynamic multi-objective optimisation algorithms. ACM Comput Surv 46(3):37

    Article  MATH  Google Scholar 

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

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

    Article  Google Scholar 

  • Jin Y, Sendhoff B (2004) Constructing dynamic optimization test problems using the multi-objective optimization concept. In: Proceedings of the EvoWorkshops, pp 525–536

  • Konak A, Coit DW, Smith AE (2006) Multi-objective optimization using genetic algorithms: a tutorial. Reliab Eng Syst Saf 91(9):992–1007

    Article  Google Scholar 

  • Koo WT, Goh C, Tan K (2010) A predictive gradient strategy for multi-objective evolutionary algorithms in a fast changing environment. Memet Comput 2(2):87–110

    Article  Google Scholar 

  • Lara A, Sanchez G, Coello CAC (2010) Hcs: a new local search strategy for memetic multi-objective evolutionary algorithms. IEEE Trans Evolut Comput 14(1):112–132

    Article  Google Scholar 

  • Li Z, Chen H, Xie Z, Chen C, Sallam A (2014) Environment identification-based memory scheme for estimation of distribution algorithms in dynamic environments. Sci World J 2014:9

    Google Scholar 

  • Li C, Yang S (2012) A general framework of multipopulation methods with clustering in undetectable dynamic environments. IEEE Trans Evolut Comput 16(4):556–577

    Article  Google Scholar 

  • Metaheuristics for Dynamic Optimization (2013) 433:265–289

  • Morrison RW, Jon KAD (2000) Triggered hypermutation revisited. In: Proceedings of the IEEE congress on evolutionary computation, vol. 2, pp 1025–1032

  • Oppacher F, Wineberg M (1999) The shifting balance genetic algorithm: Improving the ga in a dynamic environment. In: Proceedings of the genetic and evolutionary computation conference, vol 1, pp 504–510

  • Peng X, Gao X, Yang S (2011) Environment identification-based memory scheme for estimation of distribution algorithms in dynamic environments. Soft Comput 15(2):311–326

    Article  Google Scholar 

  • Ramsey CL, Grefenstette JJ (1993) Case-based initialization of genetic algorithms. In: Proceedings of the 5th international conference on genetic algorithms, pp 84–91

  • Richter H (2013) Dynamic fitness landscape analysis. In: Evolutionary computation for dynamic optimization problems, vol 490, pp 269–297

  • Shang R, Jiao L, Ren Y, Li L, Wang L (2014) Quantum immune clonal coevolutionary algorithm for dynamic multiobjective optimization. Soft Comput 18(4):743–756

    Article  MATH  Google Scholar 

  • Sierra M, Coello CC (2005) Improving pso-based multi-objective optimization using crowding, mutation and epsilon-dominance. In: Proceedings of the third international conference on evolutionary multi-criterion optimization, vol 3410, pp 505–519

  • Ursem RK (2000) Multinational ga: multimodal optimization techniques in dynamic environments. In: Proceedings of the second genetic and evolutionary computation conference, pp 19–26

  • van Veldhuizen DA (1999) Multi-objective evolutionary algorithms: classification, analyses, and new innovations, Ph.D. thesis, Graduate School of engineering Air University

  • Wang Y, Li B (2009) Investigation of memory-based multi-objective optimization evolutionary algorithm in dynamic environment. In: Proceedings of the IEEE congress on evolutionary computation, pp 630–637

  • Wang Y, Li B (2010) Multi-strategy ensemble evolutionary algorithm for dynamic multi-objective optimization. Memet Comput 2(1):3–24

    Article  Google Scholar 

  • Wei J, Jia L (20113) A novel particle swarm optimization algorithm with local search for dynamic constrained multi-objective optimization problems. In: Proceedings of the IEEE congress on evolutionary computation, pp 2436–2443

  • Yang S (2008) Genetic algorithms with memory and elitism-based immigrants in dynamic environment. Evolut Comput 16(3):385–416

    Article  Google Scholar 

  • Yang S, Yao X (2008) Population-based incremental learning with associative memory for dynamic environments. IEEE Trans Evolut Comput 12(5):542–561

    Article  Google Scholar 

  • Zhang QF, Zhou AM, Jin YC (2008) Rm-meda: a regularity model-based multi-objective estimation of distribution algorithm. IEEE Trans Evolut Comput 12(1):41–63

    Article  Google Scholar 

  • Zhang Z (2008) Multi-objective optimization immune algorithm in dynamic environments and its application to greenhouse control. Appl Soft Comput 8(2):959–971

    Article  Google Scholar 

  • Zhou A, Qu B, Li H, Zhao SZ, Suganthanb PN, Zhang Q (2011) Multiobjective evolutionary algorithms: a survey of the state of the art. Swarm Evolut Comput 1(1):32–49

    Article  Google Scholar 

  • Zhou A, Jin Y, Zhang Q (2014) A population prediction strategy for evolutionary dynamic multiobjective optimization. IEEE Trans Cybern 44(1):40–53

    Article  Google Scholar 

  • Zhou A, Jin YC, Zhang Q, Sendhoff B, Tsang E (2007) Prediction-based population re-initialization for evolutionary dynamic multi-objective optimization. In: Proceedings of the 4th international conference on evolutionary multi-criterion optimization, pp 832–846

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Authors and Affiliations

  1. SOIE Lab, University of Tunis, Tunis, Tunisia

    Radhia Azzouz, Slim Bechikh & Lamjed Ben Said

Authors
  1. Radhia Azzouz
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  2. Slim Bechikh
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  3. Lamjed Ben Said
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Corresponding author

Correspondence to Radhia Azzouz.

Additional information

Communicated by V. Loia.

Appendix

Appendix

This appendix defines the dynamic multi-objective test problems used in this paper and presents their characteristics as shown in Table 8.

Table 8 Dynamic multi-objective test instances
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Azzouz, R., Bechikh, S. & Said, L.B. A dynamic multi-objective evolutionary algorithm using a change severity-based adaptive population management strategy. Soft Comput 21, 885–906 (2017). https://doi.org/10.1007/s00500-015-1820-4

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