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Advances in Differential Evolution for Solving Multiobjective Optimization Problems

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Advances in Swarm Intelligence (ICSI 2012)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 7331))

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Abstract

Differential evolution (DE) is a powerful evolutionary optimization algorithm with many successful scientific and engineering applications. This paper presents a survey of DE for solving multiobjective optimization problems (MOPs). It provides several prominent variants of the DE for solving MOPs. Then it presents an overview of the most significant engineering applications of DE. Finally, it points out the potential future research directions.

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References

  1. Stron, R., Price, K.: Differential evolution – A simple and efficient heuristic for global optimization over continuous spaces. Journal of Global Optimization 11, 341–359 (1997)

    Article  Google Scholar 

  2. Robič, T., Filipič, B.: DEMO: Differential Evolution for Multiobjective Optimization. In: Coello Coello, C.A., Hernández Aguirre, A., Zitzler, E. (eds.) EMO 2005. LNCS, vol. 3410, pp. 520–533. Springer, Heidelberg (2005)

    Chapter  Google Scholar 

  3. Tizhoosh, H.R.: Opposition-based learning: A new scheme for machine intelligence. In: Processing of Computational Intelligence for Modelling, Control and Automation, Vienna, Austria, pp. 695–701 (2005)

    Google Scholar 

  4. Dong, N., Wang, Y.P.: Multiobjective differential evolution based on opposite operation. In: International Conference on Computational Intelligence and Security, Beijing, China, pp. 123–127 (2009)

    Google Scholar 

  5. Qian, W.Y., Li, A.J.: Adaptive differential evolution algorithm for multiobjective optimization problems. Applied Mathematics and Computation 201, 431–440 (2008)

    Article  MathSciNet  MATH  Google Scholar 

  6. Abbass, H.A., Sarker, R., Newton, C.: PDE: A pareto-frontier differential evolution approach for multi-objective optimization problems. In: Proceedings of the Congress on Evolutionary Computation, pp. 831–836. IEEE Service Center Piscataway, New Jersey (2002)

    Google Scholar 

  7. Zitzler, E., Thiele, L.: Multiobjective evolutionary algorithms: A comparative case study and the strength pareto approach. IEEE Transactions on Evolutionary Computation 3, 257–271 (1999)

    Article  Google Scholar 

  8. Xue, F., Sanderson, A.C., Graves, R.J.: Pareto-based multi-objective differential evolution. In: Proceedings of the 2003 Congress on Evolutionary Computation, pp. 862–869. IEEE Press, Canberra (2003)

    Google Scholar 

  9. Yao, F., Yang, W.D., Zhang, M.: Multi-objective differential evolution used for load distribution of hot strip mills. Control Theory & Application 27, 897–902 (2010)

    Google Scholar 

  10. Deb, K., Pratap, A., Agarwal, S., et al.: A Fast and Elitist Multiobjective Genetic Algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation 6, 182–197 (2002)

    Article  Google Scholar 

  11. Li, K., Zheng, J.H.: Improved multi-objective evolutionary algorithm based on differential evolution. Computer Engineering and Applications 44, 51–56 (2008)

    Google Scholar 

  12. Li, H., Zhang, Q.F.: Multiobjective optimization problems with complicated Pareto sets, MOEA/D and NSGA-II. IEEE Transactions on Evolutionary Computation 13, 284–302 (2009)

    Article  Google Scholar 

  13. Li, H., Zhang, Q.: A Multiobjective Differential Evolution Based on Decomposition for Multiobjective Optimization with Variable Linkages. In: Runarsson, T.P., Beyer, H.-G., Burke, E.K., Merelo-Guervós, J.J., Whitley, L.D., Yao, X. (eds.) PPSN 2006. LNCS, vol. 4193, pp. 583–592. Springer, Heidelberg (2006)

    Chapter  Google Scholar 

  14. Qu, B.Y., Suganthan, P.N.: Multiobjective evolutionary algorithms based on the summation of normalized objectives and diversified selection. Information Science 80, 3170–3181 (2010)

    Article  MathSciNet  Google Scholar 

  15. Eiben, A.E., Smith, J.E.: Introduction to evolutionary computing (natural computing series). Springer, Berlin (2003)

    Google Scholar 

  16. Zhang, J.Q., Sanderson, A.C.: JADE: Adaptive differential evolution with optional external archive. IEEE Transactions on Evolutionary Computation 13, 945–958 (2009)

    Article  Google Scholar 

  17. Zaharie, D., Petcu, D.: Adaptive pareto differential evolution algorithm and its parallelization. In: Proc. 5th. Conf. Parallel Process. Appl. Math., Czestochowa, Poland, pp. 261–268 (2003)

    Google Scholar 

  18. Abbass, H.A.: The self-adaptive pareto differential evolution algorithm. In: Proc. IEEE Congr. Evol. Comput., Honolulu, HI, pp. 831–836 (2002)

    Google Scholar 

  19. Wu, L.H., Wang, Y.N., Yuan, X.F., et al.: Multiobjective Optimization of HEV Fuel Economy and Emissions Using the Self-Adaptive Differential Evolution Algorithm. IEEE Transactions on Vehicular Technology 60, 2458–2470 (2011)

    Article  Google Scholar 

  20. Huang, V.L., Qin, A.K., Suganthan, P.N., et al.: Multi-objective optimization based on self-adaptive differential evolution algorithm. In: Proceedings of the Evolutionary Computation, pp. 3601–3608 (2007)

    Google Scholar 

  21. Huang, V.L., Zhao, S.Z., Mallipeddi, R., et al.: Multi-objective optimization using self-adaptive differential evolution algorithm. In: Proceedings of the Eleventh Conference on Congress on Evolutionary Computation, pp. 190–194. IEEE Press (2009)

    Google Scholar 

  22. Zamuda, A., Brest, J., Boškovic, B., et al.: Differential Evolution for Multiobjective Optimization with Self Adaptation. In: IEEE Congress on Evolutionary Computation, pp. 3617–3624 (2007)

    Google Scholar 

  23. Xue, F., Sanderson, A.C., Bonissone, P.P., et al.: Fuzzy logic controlled multiobjective differential evolution. In: Proc. IEEE Int. Conf. Fuzzy Syst., Reno, NV, pp. 720–725 (2005)

    Google Scholar 

  24. Zitzler, E., Deb, K., Thiele, L.: Comparison of multi-objective evolutionary algorithms: empirical results. Evolutionary Computation 8, 173–195 (2000)

    Article  Google Scholar 

  25. Das, S., Suganthan, P.N.: Differential evolution: A survey of the state-of-the-art. IEEE Transactions on Evolutionary Computation 15, 4–31 (2011)

    Article  Google Scholar 

  26. Deb, K., Miettinen, K., Chaudhuri, S.: Toward an estimation of nadir objective vector using a hybrid of evolutionary and local search approaches. IEEE Transactions on Evolutionary Computation 14, 821–841 (2010)

    Article  Google Scholar 

  27. Niu, D.P., Wang, F.L., He, D.K., et al.: Chaotic differential evolution for multiobjective optimization. Control and Decision 24, 361–364 (2009)

    MathSciNet  MATH  Google Scholar 

  28. Wang, X.Z., Li, P., Yu, G.Y.: Multi-objective chaotic differential evolution algorithm with grading second mutation. Control and Decision 26, 457–463 (2011)

    MathSciNet  Google Scholar 

  29. Chang, Y.P., Wu, C.J.: Optimal multiobjective planning of large-scale passive harmonic filters using hybrid differential evolution method considering parameter and loading uncertainty. IEEE Transactions on Power Delivery 20, 408–416 (2005)

    Article  Google Scholar 

  30. Gujarathi, A.M., Babu, B.V.: Optimization of adiabatic styrene reactor: a hybrid multiobjective differential evolution (H-MODE) approach. Ind. Eng. Chem. Res. 48, 11115–11132 (2009)

    Article  Google Scholar 

  31. Santana-Quintero, L.V., Coello Coello, C.A.: An algorithm based on differential evolution for multiobjective problems. International Journal of Computational Intelligence Research 1, 151–169 (2005)

    Article  MathSciNet  Google Scholar 

  32. Meng, H.Y., Zhang, X.H., Liu, S.Y.: A differential evolution based on double populations for constrained multi-objective optimization problem. Chinese Journal of Computer 31, 228–235 (2008)

    Article  Google Scholar 

  33. Wu, L.H., Wang, Y.N., Zhou, S.W., et al.: Research and application of pseudo parallel differential evolution algorithm with dual subpopulations. Control Theory & Applications 24, 453–458 (2007)

    Google Scholar 

  34. Goudos, S.K., Sahalos, J.N.: Pareto Optimal Microwave Filter Design Using Multiobjective Differential Evolution. IEEE Transactions on Antennas and Propagation 58, 132–144 (2010)

    Article  Google Scholar 

  35. Suresh, K., Kundu, D., Ghosh, S., et al.: Multi-Objective Differential Evolution for Automatic Clustering with Application to Micro-Array Data Analysis. Sensors 9, 3981–4004 (2009)

    Article  Google Scholar 

  36. Niu, D.P., Wang, F.L., He, D.K., et al.: Optimization of nosiheptide fermentation process based on the improved differential evolution algorithm for multi-objective optimization. Control Theory & Applications 27, 505–508 (2010)

    Google Scholar 

  37. Chen, Y., Bo, Y.M., Zou, W.J., et al.: Satisfactory Optimization for PID Regulator with Constraints on Exceeding Tolerance Characteristic Indices. Information and Control 39, 581–587 (2010)

    Google Scholar 

  38. Zhao, S.Z., Qu, B.Y., Suganthan, P.N., et al.: Multi-objective robust PID controller tuning using multi-objective differential evolution. In: International Conference on Control, Automation, Robotics and Vision, pp. 2398–2403 (2011)

    Google Scholar 

  39. Qiu, W., Zhang, J.H., Liu, N.: A Self-Adaptive Multi-0bjective Differential Evolution Algorithm for Reactive Power Optimization Considering Voltage Stability. Power System Technology 35, 81–87 (2011)

    Google Scholar 

  40. Basu, M.: Economic environmental dispatch using multi-objective differential evolution. Applied Soft Computing 11, 2845–2853 (2011)

    Article  Google Scholar 

  41. Krink, T., Paterlini, S.: Multiobjective optimization using differential evolution for real-world portfolio optimization. Computational Management Science 8, 157–179 (2011)

    Article  MathSciNet  Google Scholar 

  42. Coello Coello, C.A., Lamont, G.B., Van Veldhuizen, D.A.: Evolutionary Algorithms for Solving Multi-Objective Problems. Springer, Berlin (2007)

    MATH  Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Electronic Information and Control Engineering, Guangxi University of Technology, Liuzhou, 545006, China

    Hongtao Ye & Yan Wu

  2. Office of the President, Guangxi University of Technology, Liuzhou, 545006, China

    Meifang Zhou

Authors
  1. Hongtao Ye
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  2. Meifang Zhou
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  3. Yan Wu
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Editor information

Editors and Affiliations

  1. Key Laboratory of Machine Perception (MOE), Peking University, Department of Machine Intelligence, School of Electronics Engineering and Computer Science, Peking University, 100871, Beijing, China

    Ying Tan

  2. Department of Electrical and Electronic Engineering, Xi’an Jiaotong-Liverpool University, Suzhou, China

    Yuhui Shi

  3. Shenzhen City Key Laboratory of Embedded System Design, College of Computer Science and Software Engineering, Shenzhen University, 518060, Shenzhen, China

    Zhen Ji

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© 2012 Springer-Verlag Berlin Heidelberg

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Ye, H., Zhou, M., Wu, Y. (2012). Advances in Differential Evolution for Solving Multiobjective Optimization Problems. In: Tan, Y., Shi, Y., Ji, Z. (eds) Advances in Swarm Intelligence. ICSI 2012. Lecture Notes in Computer Science, vol 7331. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-30976-2_44

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  • DOI: https://doi.org/10.1007/978-3-642-30976-2_44

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-30975-5

  • Online ISBN: 978-3-642-30976-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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