Skip to main content
SpringerLink
Log in
Book cover

Recent Advances in Evolutionary Multi-objective Optimization pp 31–70Cite as

Dynamic Multi-objective Optimization Using Evolutionary Algorithms: A Survey

  • Chapter
  • First Online:

Part of the book series: Adaptation, Learning, and Optimization ((ALO,volume 20))

Abstract

Dynamic Multi-objective Optimization is a challenging research topic since the objective functions, constraints, and problem parameters may change over time. Although dynamic optimization and multi-objective optimization have separately obtained a great interest among many researchers, there are only few studies that have been developed to solve Dynamic Multi-objective Optimisation Problems (DMOPs). Moreover, applying Evolutionary Algorithms (EAs) to solve this category of problems is not yet highly explored although this kind of problems is of significant importance in practice. This paper is devoted to briefly survey EAs that were proposed in the literature to handle DMOPs. In addition, an overview of the most commonly used test functions, performance measures and statistical tests is presented. Actual challenges and future research directions are also discussed.

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

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD   109.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

References

  1. Fogel, L.J., Owens, A.J., Walsh, M.J.: Artificial Intelligence Through Simulated Evolution. Wiley, New York (1966)

    MATH  Google Scholar 

  2. Helbig, M., Engelbrecht, A.: Dynamic multi-objective optimization using pso. Metaheuristics Dyn. Optim. 433, 147–188 (2013)

    Article  Google Scholar 

  3. Trojanowski, K., Wierzchon, S.: Immune-based algorithms for dynamic optimization. Inf. Sci. 179(10), 1495–1515 (2009)

    Article  Google Scholar 

  4. Liu, R., Fan, J., Jiao, L.: Integration of improved predictive model and adaptive differential evolution based dynamic multi-objective evolutionary optimization algorithm. Appl. Intell. 43(1), 192–207 (2015)

    Article  Google Scholar 

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

    Article  Google Scholar 

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

    Google Scholar 

  7. Azzouz, R., Bechikh, S., Said, L.B.: Multi-objective optimization with dynamic constraints and objectives: new challenges for evolutionary algorithms. In: Genetic and Evolutionary Computation Conference (GECCO 2015), pp. 615–622 (2015)

    Google Scholar 

  8. Hatzakis, I., Wallace, D.: Dynamic multi-objective optimization with evolutionary algorithms: a forward-looking approach. In: Proceedings of the 2006 Genetic and Evolutionary Computation Conference, pp. 1201–1208 (2006)

    Google Scholar 

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

    Article  Google Scholar 

  10. Goh, C.K., Tan, K.C.: A competitive-cooperative coevolutionary paradigm for dynamic multi-objective optimization. IEEE Trans. Evol. Comput. 13(1), 103–127 (2009)

    Article  Google Scholar 

  11. Cámara, M., Ortega, J., de Toro, F.: Parallel processing for multi-objective optimization in dynamic environments. In: Proceedings of the IEEE International Parallel and Distributed Processing Symposium, pp. 1–8 (2007)

    Google Scholar 

  12. Shengxiang, Y., Soon Ong, Y., Jin, Y.: Evolutionary Computation in Dynamic and Uncertain Environments. Studies in Computational Intelligence, vol. 51. Springer, Berlin (2007)

    MATH  Google Scholar 

  13. Cruz, C., Gonzalez, J.R., Pelta, D.A.: Optimization in dynamic environments: a survey on problems, methods and measures. Soft Comput. 15(7), 1427–1448 (2011)

    Article  Google Scholar 

  14. Helbig, M., Engelbrecht, A.P.: Population-based metaheuristics for continuous boundary-constrained dynamic multi-objective optimisation problems. Swarm Evol. Comput. 14, 31–47 (2014)

    Article  Google Scholar 

  15. Carlo, R., Xin, Y.: Dynamic Multi-objective Optimization: A survey of the state-of-the-Art. Evolutionary Computation for Dynamic and Optimization Problems, pp. 85–106. Springer, Berlin (2013)

    Google Scholar 

  16. Hendrik, R.: Dynamic fitness landscape analysis. Evol. Comput. Dyn. Optim. Probl. 490, 269–297 (2013)

    Google Scholar 

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

    Article  Google Scholar 

  18. Grefenstette, J.J.: Genetic algorithms for changing environments. In: Proceedings of the Second International Conference on Parallel Problem Solving from Nature, pp. 137–144 (1992)

    Google Scholar 

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

    Article  Google Scholar 

  20. Cobb, H.G.: An investigation into the use of hypermutation as an adaptive operator in genetic algorithms having continuous, time-dependent nonstationary environments. Technical Report AIC-90-001, Naval Research Laboratory (1990)

    Google Scholar 

  21. Morrison, R.W., Jon, K.A.D.: Triggered hypermutation revisited. Proc. IEEE Congr. Evol. Comput. 2, 1025–1032 (2000)

    Google Scholar 

  22. Ramsey, C.L., Grefenstette, J.J.: Case-based initialization of genetic algorithms. In: Proceedings of the 5th International Conference on Genetic Algorithms, pp. 84–91 (1993)

    Google Scholar 

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

    Article  Google Scholar 

  24. Oppacher, F., Wineberg, M.: The shifting balance genetic algorithm: improving the ga in a dynamic environment. Proc. Genet. Evol. Comput. Conf. 1, 504–510 (1999)

    Google Scholar 

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

    Article  Google Scholar 

  26. Bosman, P.A.N.: Learning and anticipation in online dynamic optimization. In: Evolutionary Computation in Dynamic and Uncertain Environments, pp. 129–152 (2007)

    Google Scholar 

  27. Zhang, Q.F., Zhou, A.M., Jin, Y.C.: Rm-meda: a regularity model-based multi-objective estimation of distribution algorithm. IEEE Trans. Evol. Comput. 12(1), 41–63 (2008)

    Article  Google Scholar 

  28. Deb, K.: Multi-objective genetic algorithms: problem difficulties and construction of test problems. Evol. Comput. 7(3), 205–230 (1999)

    Article  Google Scholar 

  29. Woldesenbet, Y.G., Yen, G.G., Tessema, B.: Constraint handling in multi-objective evolutionary optimization. IEEE Trans. Evol. Comput. 13(3), 514–525 (2009)

    Article  Google Scholar 

  30. Chen, H., Li, M., Chen, X.: Using diversity as an additional-objective in dynamic multi-objective optimization algorithms. In: Second International Symposium on Electronic Commerce and Security, ISECS ’09, vol. 1, pp. 484–487 (2009)

    Google Scholar 

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

    Google Scholar 

  32. Sierra M., Coello, C.C.: Improving pso-based multi-objective optimization using crowding, mutation and epsilon-dominance. In: 3rd International Conference On Evolutionary multi-criterion optimization, vol. 3410, pp. 505–519 (2005)

    Google Scholar 

  33. Mehnen, J., Wagner, T., Rudolph, G.: Evolutionary optimization of dynamic multi-objective test functions. In: Proceedings of the second Italian Workshop on Evolutionary Computation (2006)

    Google Scholar 

  34. Zhou, A., Jin, Y.C., Zhang, Q., Sendhoff, B., Tsang, E.: 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 (2007)

    Google Scholar 

  35. Li, H., Zhang, Q.: A multiobjective differential evolution based on decomposition for multiobjective optimization with variable linkages. Parallel Probl. Solving Nat. 4193, 583–592 (2006)

    Google Scholar 

  36. Roy, R., Mehnen, J.: Dynamic multi-objective optimisation for machining gradient materials. CIRP Ann. Manuf. Technol. 57(1), 429–432 (2008)

    Article  Google Scholar 

  37. Liu, C.: New dynamic multiobjective evolutionary algorithm with core estimation of distribution. In: International Conference on Electrical and Control Engineering (ICECE), pp. 1345–1348 (2010)

    Google Scholar 

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

    Google Scholar 

  39. Helbig, M., Engelbrecht, A.P.: Benchmarks for dynamic multi-objective optimisation algorithms. ACM Comput. Surv. 46(3), 37:1–37:39 (2014)

    Google Scholar 

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

    Article  Google Scholar 

  41. Li, Z., Chen, H., Xie, Z., Chen, C., Sallam, A.: Dynamic multiobjective optimization algorithm based on average distance linear prediction model. Sci. World J. 2014, 9 (2014)

    Google Scholar 

  42. Muruganantham, A., Tan, K.C., Vadakkepat, P.: Solving the ieee cec 2015 dynamic benchmark problems using kalman filter based dynamic multiobjective evolutionary algorithm. Intell. Evol. Syst. 5, 239–252 (2015)

    Article  Google Scholar 

  43. Hatzakis, I., Wallace, D.: Topology of anticipatory populations for evolutionary dynamic multi-objective optimization. In: Proceedings of the 11th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference (2006)

    Google Scholar 

  44. Tan, K., Chew, Y., Lee, T., Yang, Y.: A cooperative coevolutionary algorithm for multiobjective optimization. IEEE Int. Conf. Syst. Man Cybern. 1, 390–395 (2003)

    Google Scholar 

  45. Knowles, J., Corne, D.: The pareto archived evolution strategy: a new baseline algorithm for pareto multiobjective optimisation. In: Proceedings of the 1999 Congress on Evolutionary Computation, CEC 99, vol. 1, p. 105, (1999)

    Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

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

    Google Scholar 

  49. Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A fast and elitist multiobjective genetic algorithm: Nsga-ii. IEEE Trans. Evol. Comput. 6(2), 182–197 (2002)

    Article  Google Scholar 

  50. Azzouz, R., Bechikh, S., Said, L.B.: A dynamic multi-objective evolutionary algorithm using a change severity-based adaptive population management strategy. In: Soft Computing, pp. 1–22 (2015)

    Google Scholar 

  51. Zitzler, E., Thiele, L.: Multiobjective evolutionary algorithms: a comparative case study and the strength pareto approach. IEEE Trans. Evol. Comput. 3(4), 257–271 (1999)

    Article  Google Scholar 

  52. Alba, E.: Parallel evolutionary algorithms can achieve super-linear performance. Inf. Process. Lett. 82(1), 7–13 (2002)

    Article  MathSciNet  MATH  Google Scholar 

  53. Zheng, B.: A new dynamic multi-objective optimization evolutionary algorithm. In: Proceedings of the Third International Conference on Natural Computation, pp. 565–570 (2007)

    Google Scholar 

  54. Cámara, M., Ortega, J., de Toro, F.: Parallel multi-objective optimization evolutionary algorithms in dynamic environments. Proc. First Int. Workshop Parallel Archit. Bioinspired Algorithms 1, 13–20 (2008)

    Google Scholar 

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

    MathSciNet  MATH  Google Scholar 

  56. Liu, C.-A., Wang, Y.: New evolutionary algorithm for dynamic multiobjective optimization problems. Adv. Nat. Comput. 4221, 889–892 (2006)

    Article  Google Scholar 

  57. Liu, C.-A., Wang, Y.: Dynamic multi-objective optimization evolutionary algorithm. In: Third International Conference on Natural Computation, ICNC 2007, vol. 4, pp. 456–459 (2007)

    Google Scholar 

  58. Liu, C.A., Wang, Y., Ren, A.: New dynamic multi-objective constrained optimization evolutionary algorithm. Asia-Pac. J. Oper. Res. 32(05) (2015)

    Google Scholar 

  59. Zitzler, E.: Evolutionary algorithms for multiobjective optimization: methods and applications. Ph.D. thesis, Swiss Federal Institute of Technology (ETH) Zurich, Switzerland (1999)

    Google Scholar 

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

    Article  Google Scholar 

  61. Zeng, S., Yao, S., Kang, L., Liu, Y.: An efficient multi-objective evolutionary algorithm: Omoea-ii. In: Third International Conference on Evolutionary Multi-criterion Optimization (EMO 2005), pp. 108–119 (2005)

    Google Scholar 

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

    Article  Google Scholar 

  63. Zeng, S.Y., Chen, G., Zheng, L., Shi, H., de Garis, H., Ding, L., Kang, L.: A dynamic multi-objective evolutionary algorithm based on an orthogonal design. In: Proceedings of the IEEE Congress on Evolutionary Computation, pp. 573–580 (2006)

    Google Scholar 

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

    Google Scholar 

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

    Article  Google Scholar 

  66. Azzouz, R., Bechikh, S., Said, L.B.: 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 (2014)

    Google Scholar 

  67. Xiaodong, L., Branke, J., Kirley, M.: On performance metrics and particle swarm methods for dynamic multiobjective optimization problems. IEEE Congr. Evol. Comput. CEC 2007, 576–583 (2007)

    Google Scholar 

  68. Liu, C.-A.: New dynamic multiobjective evolutionary algorithm with core estimation of distribution. In: International Conference on Electrical and Control Engineering (ICECE), pp. 1345–1348 (2010)

    Google Scholar 

  69. Tang, G.C.M., Huang, Z.: The construction of dynamic multi-objective optimization test functions. Adv. Comput. Intell. 4683, 72–79 (2007)

    Google Scholar 

  70. Avdagic, S.O.Z., Konjicija, S.: Evolutionary approach to solving non-stationary dynamic multi-objective problems. Found. Comput. Intell. 3(203), 267–289 (2009)

    Google Scholar 

  71. Helbig, M., Engelbrecht, A.: Archive management for dynamic multi-objective optimisation problems using vector evaluated particle swarm optimisation. In: IEEE Congress on Evolutionary Computation (CEC), pp. 2047–2054 (2011)

    Google Scholar 

  72. Helbig, M., Engelbrecht, A.: Benchmarks for dynamic multi-objective optimisation. In: IEEE Symposium on Computational Intelligence in Dynamic and Uncertain Environments (CIDUE), pp. 84–91 (2013)

    Google Scholar 

  73. Biswas, S., Das, S., Suganthan, P., Coello, C.C.: Evolutionary multiobjective optimization in dynamic environments: a set of novel benchmark functions. In: 2014 IEEE Congress on Evolutionary Computation (CEC), pp. 3192–3199 (2014)

    Google Scholar 

  74. Hamalainen, R.P., Mantysaari, J.: A dynamic interval goal programming approach to the regulation of a lake - river system. J. Multi-criteria Decis. Anal. 10, 75–86 (2001)

    Article  MATH  Google Scholar 

  75. Hamalainen, R.P., Mantysaari, J.: Dynamic multi-objective heating optimization. Eur. J. Oper. Res. 142(1), 1–15 (2002)

    Article  MATH  Google Scholar 

  76. Ursem, R., Krink, T., Filipic, B.: A numerical simulator for a crop-producing greenhouse. In: EVALife Technical Report, no. 2002-01 (2002)

    Google Scholar 

  77. Shen, X.-N., Yao, X.: Mathematical modeling and multi-objective evolutionary algorithms applied to dynamic flexible job shop scheduling problems. Inf. Sci. 298, 198–224 (2015)

    Article  MathSciNet  Google Scholar 

  78. Nguyen, S., Zhang, M., Tan, K.C.: Enhancing genetic programming based hyper-heuristics for dynamic multi-objective job shop scheduling problems. In: 2015 IEEE Congress on Evolutionary Computation (CEC), pp. 2781–2788 (2015)

    Google Scholar 

  79. Palaniappan, S., Zein-Sabatto, S., Sekmen, A.: Dynamic multiobjective optimization of war resource allocation using adaptive genetic algorithms. In: Proceedings of IEEE SoutheastCon, pp. 160–165 (2001)

    Google Scholar 

  80. Hutzschenreuter, A., Bosman, P., Poutré, H.: Evolutionary multiobjective optimization for dynamic hospital resource management. In: Proceedings of International Conference on Multi-criterion Optimization, pp. 320–334 (2009)

    Google Scholar 

  81. Wahle, J., Annen, O., Schuster, C., Neubert, L., Schreckenberg, M.: A dynamic route guidance system based on real traffic data. Eur. J. Oper. Res. 131(2), 302–308 (2001)

    Article  MATH  Google Scholar 

  82. Constantinou, D.: Ant colony optimisation algorithms for solving multi-objective power aware metrics for mobile ad hoc networks. Ph.D. thesis, Department of Computer Science, University of Pretoria, South Africa (2011)

    Google Scholar 

  83. Grimme, C., Meisel, S., Trautmann, H., Rudolph, G., Wölck, M.: Multi-objective analysis of approaches to dynamic routing of a vehicle. In: Twenty-Third European Conference on Information Systems Completed Research Papers. Paper 62 (2015)

    Google Scholar 

  84. Meisel, S., Grimme, C., Bossek, J., Wölck, M., Rudolph, G., Trautmann, H.: Evaluation of a multi-objective ea on benchmark instances for dynamic routing of a vehicle. In: Proceedings of the 2015 Annual Conference on Genetic and Evolutionary Computation, pp. 425–432 (2015)

    Google Scholar 

  85. Chen, C.-L., Lee, W.-C.: Multi-objective optimization of multi-echelon supply chain networks with uncertain product demands and prices. Comput. Chem. Eng. 28, 1131–1144 (2004)

    Article  Google Scholar 

  86. Selim, H., Araz, C., Ozkarahan, I.: Collaborative production-distribution planning in supply chain: a fuzzy goal programming approach. Transp. Res. Part E: Logist. Transp. Rev. 44(3), 396–419 (2008)

    Article  Google Scholar 

  87. Maalawi, K.: Special issue on design optimization of wind turbine structures. In: Wind Turbines (2011)

    Google Scholar 

  88. Zhang, Z., Qian, S.: Artificial immune system in dynamic environments solving time-varying non-linear constrained multi-objective problems. Soft Comput. 15(7), 1333–1349 (2011)

    Article  Google Scholar 

  89. Weicker, K.: Performance measures for dynamic environments. In: Parallel Problem Solving from Nature, pp. 64–73 (2002)

    Google Scholar 

  90. Cámara, M., Ortega, J., Toro, F.d.: Approaching dynamic multi-objective optimization problems by using parallel evolutionary algorithms. In: Advances in Multi-objective Nature Inspired Computing, vol. 272, pp. 63–86 (2010)

    Google Scholar 

  91. Bechikh, S., Kessentini, M., Said, L.B., Ghedira, K.: Preference incorporation in evolutionary multiobjective optimization: a survey of the state-of-the-art. Advances in Computers, vol. 98, pp. 141–207. Elsevier (2015)

    Google Scholar 

  92. Bechikh, S.: Incorporating Decision Maker’s Preference Information in Evolutionary Multi-objective Optimization. Ph.D. thesis, University of Tunis, ISG-Tunis, Tunisia (2013)

    Google Scholar 

  93. Bechikh, S., Said, L.B., Ghedira, K.: Negotiating decision makers’ reference points for group preference-based evolutionary multi-objective optimization. In: 2011 11th International Conference on Hybrid Intelligent Systems (HIS), pp. 377–382 (2011)

    Google Scholar 

  94. Bechikh, S., Said, L.B., Ghedira, K.: Group preference-based evolutionary multi-objective optimization with non-equally important decision makers: Application to the portfolio selection problem. Int. J. Comput. Inf. Syst. Ind. Manag. Appl. 5(1), 278–288 (2013)

    Google Scholar 

  95. Trabelsi, W., Azzouz, R., Bechikh, S., Said, L.B.: Leveraging evolutionary algorithms for dynamic multi-objective optimization scheduling of multi-tenant smart home appliances. In: IEEE Congress on Evolutionary Computation (2016)

    Google Scholar 

  96. Azzouz, R., Bechikh, S., Said, L.B.: Articulating decision maker’s preference information within multiobjective artificial immune systems. In: 2012 IEEE 24th International Conference on Tools with Artificial Intelligence, vol. 1, pp. 327–334 (2012)

    Google Scholar 

  97. Bechikh, S., Chaabani, A., Said, L.B.: An efficient chemical reaction optimization algorithm for multiobjective optimization. IEEE Trans. Cybern. 45(10), 2051–2064 (2015)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. SOIE Lab, Computer Science Department, ISG-Tunis, University of Tunis, Bouchoucha City, 2000, Le Bardo, Tunis, Tunisia

    Radhia Azzouz, Slim Bechikh & Lamjed Ben Said

Authors
  1. Radhia Azzouz
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Slim Bechikh
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Lamjed Ben Said
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Radhia Azzouz .

Editor information

Editors and Affiliations

  1. SOIE lab, Computer Science Department, University of Tunis, ISG-Tunis , Bouchoucha, Le Bardo, Tunisia

    Slim Bechikh

  2. Department of Mechanical Engineering, Institute of Technology, Kalyanpur, Kanpur, Uttar Pradesh, India

    Rituparna Datta

  3. School of Computer Engineering, Nanyang Technological University, Singapore, Singapore

    Abhishek Gupta

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer International Publishing Switzerland

About this chapter

Cite this chapter

Azzouz, R., Bechikh, S., Ben Said, L. (2017). Dynamic Multi-objective Optimization Using Evolutionary Algorithms: A Survey. In: Bechikh, S., Datta, R., Gupta, A. (eds) Recent Advances in Evolutionary Multi-objective Optimization. Adaptation, Learning, and Optimization, vol 20. Springer, Cham. https://doi.org/10.1007/978-3-319-42978-6_2

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-42978-6_2

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-42977-9

  • Online ISBN: 978-3-319-42978-6

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation