Skip to main content
SpringerLink
Log in
Book cover

Evolutionary Multiobjective Optimization pp 7–32Cite as

Recent Trends in Evolutionary Multiobjective Optimization

  • Chapter

Part of the book series: Advanced Information and Knowledge Processing ((AI&KP))

Summary

This chapter presents a brief review of some of the most relevant research currently taking place in evolutionary multiobjective optimization. The main topics covered include algorithms, applications, metrics, test functions, and theory. Some of the most promising future paths of research are also addressed.

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   109.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   139.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD   199.99
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

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Goldberg, DE, Genetic Algorithms in Search, Optimization and Machine Learning. Addison-Wesley Publishing Company, Reading, MA, 1989.

    Google Scholar 

  2. Bäck, T, Fogel, DB, and Michalewicz, Z (eds.), Handbook of Evolutionary Computation. Institute of Physics Publishing and Oxford University Press, 1997.

    Google Scholar 

  3. Mitchell, M, An Introduction to Genetic Algorithms. MIT Press, Cambridge, MA, 1996.

    Google Scholar 

  4. Schaffer, JD, Multiple Objective Optimization with Vector Evaluated Genetic Algorithms. PhD thesis, Vanderbilt University, Nashville, TN, 1984.

    Google Scholar 

  5. Schaffer, JD, Multiple objective optimization with vector evaluated genetic algorithms. In Genetic Algorithms and their Applications: Proceedings of the First International Conference on Genetic Algorithms, pp. 93–100, Hillsdale, NJ, 1985. Lawrence Erlbaum.

    Google Scholar 

  6. Coello Coello, CA, Van Veldhuizen, DA, and Lamont, GB, Evolutionary Algorithms for Solving Multi-Objective Problems. Kluwer Academic Publishers, New York, May 2002. ISBN 0-3064-6762-3.

    Google Scholar 

  7. Miettinen, KM, Nonlinear Multiobjective Optimization. Kluwer Academic Publishers, Boston, Massachusetts, 1998.

    Google Scholar 

  8. Coello Coello, CA, A Comprehensive Survey of Evolutionary-Based Multiobjective Optimization Techniques. Knowledge and Information Systems. An International Journal, 1(3):269–308, August 1999.

    Google Scholar 

  9. Deb, K, Multi-Objective Optimization using Evolutionary Algorithms. John Wiley & Sons, Chichester, UK, 2001. ISBN 0-471-87339-X.

    Google Scholar 

  10. Edgeworth, FY, Mathematical Physics. P. Keagan, London, England, 1881.

    Google Scholar 

  11. Pareto, V, Cours D’Economie Politique, volume I and II. F. Rouge, Lausanne, 1896.

    Google Scholar 

  12. Stadler, W, Fundamentals of multicriteria optimization. In Stadler, W (ed), Multicriteria Optimization in Engineering and the Sciences, pp. 1–25. Plenum Press, New York, NY, 1988.

    Google Scholar 

  13. Rosenberg, RS, Simulation of genetic populations with biochemical properties. PhD thesis, University of Michigan, Ann Arbor, MI, 1967.

    Google Scholar 

  14. Schaffer, JD and Grefenstette, JJ, Multiobjective learning via genetic algorithms. In Proceedings of the 9th International Joint Conference on Artificial Intelligence (IJCAI-85), pp. 593–595, Los Angeles, CA, 1985. AAAI.

    Google Scholar 

  15. Coello Coello, CA and Mariano Romero, CE, Evolutionary Algorithms and Multiple Objective Optimization. In Ehrgott, M and Gandibleux, X (eds.), Multiple Criteria Optimization: State of the Art Annotated Bibliographic Surveys, pp. 277–331. Kluwer Academic Publishers, Boston, 2002.

    Google Scholar 

  16. Kuhn, HW and Tucker, AW, Nonlinear programming. In Neyman, J (ed), Proceedings of the Second Berkeley Symposium on Mathematical Statistics and Probability, pp. 481–492, Berkeley, CA, 1951. University of California Press.

    Google Scholar 

  17. Rao, SS, Game theory approach for multiobjective structural optimization. Computers and Structures, 25(1):119–127, 1987.

    Article  Google Scholar 

  18. Périaux, J, Sefrioui, M, and Mantel, B, GA multiple objective optimization strategies for electromagnetic backscattering. In Quagliarella, D, Périaux, J, Poloni, C, and Winter, G (eds.), Genetic Algorithms and Evolution Strategies in Engineering and Computer Science. Recent Advances and Industrial Applications, chapter 11, pp. 225–243. John Wiley and Sons, West Sussex, England, 1997.

    Google Scholar 

  19. Deb, K, Solving goal programming problems using multi-objective genetic algorithms. In 1999 Congress on Evolutionary Computation, pp. 77–84, Piscataway, NJ, July 1999. IEEE Service Center.

    Google Scholar 

  20. Wienke, PB, Lucasius, C, and Kateman, G, Multicriteria target optimization of analytical procedures using a genetic algorithm. Analytical Chimica Acta, 265(2):211–225, 1992.

    Article  Google Scholar 

  21. Wilson, PB and Macleod, MD, Low implementation cost IIR digital filter design using genetic algorithms. In IEE/IEEE Workshop on Natural Algorithms in Signal Processing, pp. 4/1–4/8, Chelmsford, U.K., 1993.

    Google Scholar 

  22. Zebulum, RS, Pacheco, MA, and Vellasco, M, A multi-objective optimisation methodology applied to the synthesis of low-power operational amplifiers. In Cheuri, IJ and dos Reis Filho, CA (eds.), Proceedings of the XIII International Conference in Microelectronics and Packaging, volume 1, pp. 264–271, Curitiba, Brazil, August 1998.

    Google Scholar 

  23. Hajela, P and Lin, CY, Genetic search strategies in multicriterion optimal design. Structural Optimization, 4:99–107, 1992.

    Article  Google Scholar 

  24. Coello Coello, CA and Christiansen, AD, Two new GA-based methods for multiobjective optimization. Civil Engineering Systems, 15(3):207–243, 1998.

    Google Scholar 

  25. Das, I and Dennis, J, A closer look at drawbacks of minimizing weighted sums of objectives for pareto set generation in multicriteria optimization problems. Structural Optimization, 14(1):63–69, 1997.

    Article  Google Scholar 

  26. Jin, Y, Okabe, T, and Sendhoff, B, Dynamic Weighted Aggregation for Evolutionary Multi-Objective Optimization: Why Does It Work and How? In Spector, L, Goodman, ED, Wu, A, Langdon, WB, Voigt, HM, Gen, M, Sen, S, Dorigo, M, Pezeshk, S, Garzon, MH, and Burke, E (eds.), Proceedings of the Genetic and Evolutionary Computation Conference (GECCO’2001), pp. 1042–1049, San Francisco, California, 2001. Morgan Kaufmann Publishers.

    Google Scholar 

  27. Coello Coello, CA, Treating Constraints as Objectives for Single-Objective Evolutionary Optimization. Engineering Optimization, 32(3):275–308, 2000.

    Google Scholar 

  28. Norris, SR and Crossley, WA, Pareto-optimal controller gains generated by a genetic algorithm. In AIAA 36th Aerospace Sciences Meeting and Exhibit, Reno, Nevada, January 1998. AIAA Paper 98-0010.

    Google Scholar 

  29. Rogers, JL, A parallel approach to optimum actuator selection with a genetic algorithm. In AIAA Paper No. 2000-4484, AIAA Guidance, Navigation, and Control Conference, Denver, CO, August 14–17 2000.

    Google Scholar 

  30. Sridhar, J and Rajendran, C, Scheduling in Flowshop and Cellular Manufacturing Systems with Multiple Objectives — A Genetic Algorithmic Approach. Production Planning & Control, 7(4):374–382, 1996.

    Google Scholar 

  31. Venugopal, V and Narendran, TT, A genetic algorithm approach to the machine-component grouping problem with multiple objectives. Computers and Industrial Engineering, 22(4):469–480, 1992.

    Article  Google Scholar 

  32. Deb, K and Goldberg, DE, An investigation of niche and species formation in genetic function optimization. In Schaffer, JD (ed), Proceedings of the Third International Conference on Genetic Algorithms, pp. 42–50, San Mateo, CA, June 1989. Morgan Kaufmann Publishers.

    Google Scholar 

  33. Srinivas, N and Deb, K, Multiobjective optimization using nondominated sorting in genetic algorithms. Evolutionary Computation, 2(3):221–248, Fall 1994.

    Google Scholar 

  34. Horn, J, Nafpliotis, N, and Goldberg, DE, A niched pareto genetic algorithm for multiobjective optimization. In Proceedings of the First IEEE Conference on Evolutionary Computation, IEEE World Congress on Computational Intelligence, volume 1, pp. 82–87, Piscataway, NJ, June 1994. IEEE Service Center.

    Google Scholar 

  35. Goldberg, DE and Richardson, J, Genetic algorithm with sharing for multimodal function optimization. In Grefenstette, JJ (ed), Genetic Algorithms and Their Applications: Proceedings of the Second International Conference on Genetic Algorithms, pp. 41–49, Hillsdale, NJ, 1987. Lawrence Erlbaum.

    Google Scholar 

  36. Fonseca, CM and Fleming, PJ, Genetic algorithms for multiobjective optimization: Formulation, discussion and generalization. In Forrest, S (ed), Proceedings of the Fifth International Conference on Genetic Algorithms, pp. 416–423, San Mateo, CA, 1993. Morgan Kaufmann Publishers.

    Google Scholar 

  37. Zitzler, E and Thiele, L, Multiobjective evolutionary algorithms: A comparative case study and the strength pareto approach. IEEE Transactions on Evolutionary Computation, 3(4):257–271, November 1999.

    Article  Google Scholar 

  38. Morse, JN, Reducing the size of the nondominated set: Pruning by clustering. Computers and Operations Research, 7(1–2):55–66, 1980.

    Article  Google Scholar 

  39. Zitzler, E, Laumanns, M, and Thiele, L, SPEA2: Improving the Strength Pareto Evolutionary Algorithm. Technical Report 103, Computer Engineering and Networks Laboratory (TIK), Swiss Federal Institute of Technology (ETH) Zurich, Gloriastrasse 35, CH-8092 Zurich, Switzerland, May 2001.

    Google Scholar 

  40. Knowles, JD and Corne, DW, Approximating the nondominated front using the pareto archived evolution strategy. Evolutionary Computation, 8(2):149–172, 2000.

    Article  PubMed  Google Scholar 

  41. Deb, K, Agrawal, S, Pratab, A, and Meyarivan, T, A fast elitist non-dominated sorting genetic algorithm for multi-objective optimization: NSGA-II. KanGAL report 200001, Indian Institute of Technology, Kanpur, India, 2000.

    Google Scholar 

  42. Deb, K, Agrawal, S, Pratab, A, and Meyarivan, T, A fast elitist nondominated sorting genetic algorithm for multi-objective optimization: NSGAII. In Schoenauer, M, Deb, K, Rudolph, G, Yao, X, Lutton, E, Merelo, JJ, and Schwefel, H-P (eds.), Proceedings of the Parallel Problem Solving from Nature VI Conference, pp. 849–858, Paris, France, 2000. Springer. Lecture Notes in Computer Science No. 1917.

    Google Scholar 

  43. Deb, K, Pratap, A, Agarwal, S, and Meyarivan, T, A Fast and Elitist Multiobjective Genetic Algorithm: NSGA—II. IEEE Transactions on Evolutionary Computation, 6(2):182–197, April 2002.

    Article  Google Scholar 

  44. Erickson, M, Mayer, A, and Horn, J, The Niched Pareto Genetic Algorithm 2 Applied to the Design of Groundwater Remediation Systems. In Zitzler, E, Deb, K, Thiele, L, Coello Coello, CA, and Corne, D (eds.), First International Conference on Evolutionary Multi-Criterion Optimization, pp. 681–695. Springer-Verlag. Lecture Notes in Computer Science No. 1993, 2001.

    Google Scholar 

  45. Oei, CK, Goldberg, DE, and Chang, S-J, Tournament Selection, Niching, and the Preservation of Diversity. Technical Report 91011, Illinois Genetic Algorithms Laboratory, University of Illinois at Urbana-Champaign, Urbana, Illinois, December 1991.

    Google Scholar 

  46. Corne, DW, Knowles, JD, and Oates, MJ, The Pareto Envelope-based Selection Algorithm for Multiobjective Optimization. In Schoenauer, M, Deb, K, Rudolph, G, Yao, X, Lutton, E, Merelo, JJ, and Schwefel, H-P (eds.), Proceedings of the Parallel Problem Solving from Nature VI Conference, pp. 839–848, Paris, France, 2000. Springer. Lecture Notes in Computer Science No. 1917.

    Google Scholar 

  47. Corne, DW, Jerram, NR, Knowles, JD, and Oates, MJ, PESA-II: Region-based Selection in Evolutionary Multiobjective Optimization. In Spector, L, Goodman, ED, Wu, A, Langdon, WB, Voigt, H-M, Gen, M, Sen, S, Dorigo, M, Pezeshk, S, Garzon, MH, and Burke, E (eds.), Proceedings of the Genetic and Evolutionary Computation Conference (GECCO’2001), pp. 283–290, San Francisco, California, 2001. Morgan Kaufmann Publishers.

    Google Scholar 

  48. Goldberg, DE and Deb, K, A comparison of selection schemes used in genetic algorithms. In Rawlins, GJE (ed), Foundations of Genetic Algorithms, pp. 69–93. Morgan Kaufmann, San Mateo, CA, 1991.

    Google Scholar 

  49. Coello Coello, CA and Toscano Pulido, G, A Micro-Genetic Algorithm for Multiobjective Optimization. In Zitzler, E, Deb, K, Thiele, L, Coello Coello, CA, and Corne, D (eds.), First International Conference on Evolutionary Multi-Criterion Optimization, pp. 126–140. Springer-Verlag. Lecture Notes in Computer Science No. 1993, 2001.

    Google Scholar 

  50. Coello Coello, CA and Toscano Pulido, G, Multiobjective Optimization using a Micro-Genetic Algorithm. In Spector, L, Goodman, ED, Wu, A, Langdon, WB, Voigt, H-M, Gen, M, Sen, S, Dorigo, M, Pezeshk, S, Garzon, MH, and Burke, E (eds.), Proceedings of the Genetic and Evolutionary Computation Conference (GECCO’2001), pp. 274–282, San Francisco, California, 2001. Morgan Kaufmann Publishers.

    Google Scholar 

  51. Coello Coello, CA and Salazar Lechuga, M, MOPSO: A Proposal for Multiple Objective Particle Swarm Optimization. In Congress on Evolutionary Computation (CEC’2002), volume 2, pp. 1051–1056, Piscataway, New Jersey, May 2002. IEEE Service Center.

    Article  Google Scholar 

  52. Jensen, MT, Reducing the run-time complexity of multiobjective eas: The nsgaii and other algorithms. IEEE Transactions on Evolutionary Computation, 7(5):503–515, October 2003.

    Article  Google Scholar 

  53. Everson, RM, Fieldsend, JE, and Singh, S, Full Elite Sets for Multi-Objective Optimisation. In Parmee, IC (ed), Proceedings of the Fifth International Conference on Adaptive Computing Design and Manufacture (ACDM 2002), volume 5, pp. 343–354, University of Exeter, Devon, UK, April 2002. Springer-Verlag.

    Google Scholar 

  54. Fieldsend, JE, Everson, RM, and Singh, S, Using Unconstrained Elite Archives for Multiobjective Optimization. IEEE Transactions on Evolutionary Computation, 7(3):305–323, June 2003.

    Article  Google Scholar 

  55. Habenicht, W, Quad trees: A data structure for discrete vector optimization problems. In Lecture Notes in Economics and Mathematical Systems, volume 209, pp. 136–145, 1982.

    Google Scholar 

  56. Mostaghim, S, Teich, J, and Tyagi, A, Comparison of Data Structures for Storing Pareto-sets in MOEAs. In Congress on Evolutionary Computation (CEC’2002), volume 1, pp. 843–848, Piscataway, New Jersey, May 2002. IEEE Service Center.

    Article  Google Scholar 

  57. Purshouse, RC and Fleming, PJ, Why use Elitism and Sharing in a Multi-Objective Genetic Algorithm? In Langdon, WB, Cantú-Paz, E, Mathias, K, Roy, R, Davis, D, Poli, R, Balakrishnan, K, Honavar, V, Rudolph, G, Wegener, J, Bull, L, Potter, MA, Schultz, AC, Miller, JF, Burke, E, and Jonoska, N (eds.), Proceedings of the Genetic and Evolutionary Computation Conference (GECCO’2002), pp. 520–527, San Francisco, California, July 2002. Morgan Kaufmann Publishers.

    Google Scholar 

  58. Laumanns, M, Zitzler, E, and Thiele, L, On the Effects of Archiving, Elitism, and Density Based Selection in Evolutionary Multi-objective Optimization. In Zitzler, E, Deb, K, Thiele, L, Coello Coello, CA, and Corne, D (eds.), First International Conference on Evolutionary Multi-Criterion Optimization, pp. 181–196. Springer-Verlag. Lecture Notes in Computer Science No. 1993, 2001.

    Google Scholar 

  59. Fonseca, CM and Fleming, PJ, An overview of evolutionary algorithms in multiobjective optimization. Evolutionary Computation, 3(1):1–16, Spring 1995.

    Google Scholar 

  60. Thomson, R and Arslan, T, The Evolutionary Design and Synthesis of Non-Linear Digital VLSI Systems. In Lohn, J, Zebulum, R, Steincamp, J, Keymeulen, D, Stoica, A, and Ferguson, MI (eds.), Proceedings of the 2003 NASA/DoD Conference on Evolvable Hardware, pp. 125–134, Los Alamitos, California, July 2003. IEEE Computer Society Press.

    Google Scholar 

  61. Abdel-Magid, YL and Abido, MA, Optimal Multiobjective Design of Robust Power System Stabilizers Using Genetic Algorithms. IEEE Transactions on Power Systems, 18(3):1125–1132, August 2003.

    Article  Google Scholar 

  62. Ramírez Rosado, IJ and Bernal Agustín, JL, Reliability and cost optimization for distribution networks expansion using an evolutionary algorithm. IEEE Transactions on Power Systems, 16(1):111–118, February 2001.

    Article  Google Scholar 

  63. Reed, PM, Minsker, BS, and Goldberg, DE, A multiobjective approach to cost effective long-term groundwater monitoring using an elitist nondominated sorted genetic algorithm with historical data. Journal of Hydroinformatics, 3(2):71–89, April 2001.

    Google Scholar 

  64. Formiga, KTM, Chaufhry, FH, Cheung, PB, and Reis, LFR, Optimal Design of Water Distribution System by Multiobjective Evolutionary Methods. In Fonseca, CM, Fleming, PJ, Zitzler, E, Deb, K, and Thiele, L (eds.), Evolutionary Multi-Criterion Optimization. Second International Conference, EMO 2003, pp. 677–691, Faro, Portugal, April 2003. Springer. Lecture Notes in Computer Science. Volume 2632.

    Google Scholar 

  65. Kurapati, A and Azarm, S, Immune Network Simulation with Multiobjective Genetic Algorithms for Multidisciplinary Design Optimization. Engineering Optimization, 33:245–260, 2000.

    Google Scholar 

  66. Coello Coello, CA and Christiansen, AD, Multiobjective optimization of trusses using genetic algorithms. Computers and Structures, 75(6):647–660, May 2000.

    Article  Google Scholar 

  67. Aguilar Madeira, JF, Rodrigues, H, and Pina, H, Genetic Methods in Multiobjective Optimization of Structures with an Equality Constraint on Volume. In Fonseca, CM, Fleming, PJ, Zitzler, E, Deb, K, and Thiele, L (eds.), Evolutionary Multi-Criterion Optimization. Second International Conference, EMO 2003, pp. 767–781, Faro, Portugal, April 2003. Springer. Lecture Notes in Computer Science. Volume 2632.

    Google Scholar 

  68. Pulliam, TH, Nemec, M, Hoslt, T, and Zingg, DW, Comparison of Evolutionary (Genetic) Algorithm and Adjoint Methods for Multi-Objective Viscous Airfoil Optimizations. In 41st Aerospace Sciences Meeting. Paper AIAA 2003-0298, Reno, Nevada, January 2003.

    Google Scholar 

  69. Obayashi, S, Tsukahara, T, and Nakamura, T, Multiobjective evolutionary computation for supersonic wing-shape optimization. IEEE Transactions on Evolutionary Computation, 4(2):182–187, July 2000.

    Article  Google Scholar 

  70. Lavagna, MR and Ercoli Finzi, A, Concurrent Processes within Preliminary Spacecraft Design: An Autonomous Decisional Support Based on Genetic Algorithms and Analytic Hierarchical Process. In Proceedings of the 17th International Symposium on Space Flight Dynamics, Moscow, Russia, June 2003.

    Google Scholar 

  71. Osyczka, A, Krenich, S, and Karaś, K, Optimum design of robot grippers using genetic algorithms. In Proceedings of the Third World Congress of Structural and Multidisciplinary Optimization (WCSMO), Buffalo, New York, May 1999.

    Google Scholar 

  72. Teo, J and Abbass, HA, Is a Self-Adaptive Pareto Approach Beneficial for Controlling Embodied Virtual Robots. In Cantú-Paz, E et al. (eds), Genetic and Evolutionary Computation—GECCO 2003. Proceedings, Part II, pp. 1612–1613. Springer. Lecture Notes in Computer Science Vol. 2724, July 2003.

    Google Scholar 

  73. Ortmann, M and Weber, W, Multi-criterion optimization of robot trajectories with evolutionary strategies. In Proceedings of the 2001 Genetic and Evolutionary Computation Conference. Late-Breaking Papers, pp. 310–316, San Francisco, CA, July 2001.

    Google Scholar 

  74. Blumel, AL, Hughes, EJ, and White, BA, Multi-objective Evolutionary Design of Fuzzy Autopilot Controller. In Zitzler, E, Deb, K, Thiele, L, Coello Coello, CA, and Corne, D (eds.), First International Conference on Evolutionary Multi-Criterion Optimization, pp. 668–680. Springer-Verlag. Lecture Notes in Computer Science No. 1993, 2001.

    Google Scholar 

  75. Tan, KC, Lee, TH, Khor, EF, and Ou, K, Control system design unification and automation using an incremented multi-objective evolutionary algorithm. In Hamza, MH (ed), Proceedings of the 19th IASTED International Conference on Modeling, Identification and Control. IASTED, Innsbruck, Austria, 2000.

    Google Scholar 

  76. Kundu, S and Kawata, S, Evolutionary Multicriteria Optimization for Improved Design of Optimal Control Systems. In Parmee, IC (ed), Proceedings of the Fifth International Conference on Adaptive Computing Design and Manufacture (ACDM 2002), volume 5, pp. 207–218, University of Exeter, Devon, UK, April 2002. Springer-Verlag.

    Google Scholar 

  77. Caswell, DJ and Lamont, GB, Wire-Antenna Geometry Design with Multiobjective Genetic Algorithms. In Congress on Evolutionary Computation (CEC’2002), volume 1, pp. 103–108, Piscataway, New Jersey, May 2002. IEEE Service Center.

    Article  Google Scholar 

  78. Kumar, R, and Banerjee, N, Multicriteria Network Design Using Evolutionary Algorithm. In Cantú-Paz, E et al. (eds), Genetic and Evolutionary Computation—GECCO 2003. Proceedings, Part II, pp. 2179–2190. Springer. Lecture Notes in Computer Science Vol. 2724, July 2003.

    Google Scholar 

  79. Pullan, W, Optimising Multiple Aspects of Network Survivability. In Congress on Evolutionary Computation (CEC’2002), volume 1, pp. 115–120, Piscataway, New Jersey, May 2002. IEEE Service Center.

    Article  Google Scholar 

  80. Feng, CW, Liu, L, and Burns, SA, Using genetic algorithms to solve construction time-cost trade-off problems. Journal of Computing in Civil Engineering, 10(3):184–189, 1999.

    Article  Google Scholar 

  81. Balling, R, The Maximin Fitness Function; Multiobjective City and Regional Planning. In Fonseca, CM, Fleming, PJ, Zitzler, E, Deb, K, and Thiele, L (eds.), Evolutionary Multi-Criterion Optimization. Second International Conference, EMO 2003, pp. 1–15, Faro, Portugal, April 2003. Springer. Lecture Notes in Computer Science. Volume 2632.

    Google Scholar 

  82. Khajehpour, S, Optimal Conceptual Design of High-Rise Office Buldings. PhD thesis, Civil Engineering Department, University of Waterloo, Ontario, Canada, 2001.

    Google Scholar 

  83. Cheng, R, Gen, M, and Oren, SS, An Adaptive Hyperplane Approach for Multiple Objective Optimization Problems with Complex Constraints. In Whitley, D, Goldberg, D, Cantú-Paz, E, Spector, L, Parmee, I, and Beyer, H-G (eds.), Proceedings of the Genetic and Evolutionary Computation Conference (GECCO’2000), pp. 299–306, San Francisco, California, 2000. Morgan Kaufmann.

    Google Scholar 

  84. Gen, M and Li, Y-Z, Solving multi-objective transportation problems by spanning tree-based genetic algorithm. In Parmee, I (ed), The Integration of Evolutionary and Adaptive Computing Technologies with Product/System Design and Realisation, pp. 95–108, Plymouth, United Kingdom, April 1998. Plymouth Engineering Design Centre, Springer-Verlag.

    Google Scholar 

  85. Laumanns, N, Laumanns, M, and Neunzig, D, Multi-objective design space exploration of road trains with evolutionary algorithms. In Zitzler, E, Deb, K, Thiele, L, Coello Coello, CA, and Corne, D (eds.), First International Conference on Evolutionary Multi-Criterion Optimization, pp. 612–623. Springer-Verlag. Lecture Notes in Computer Science No. 1993, Berlin, Germany, 2001.

    Google Scholar 

  86. Andersson, J, Applications of a Multi-objective Genetic Algorithm to Engineering Design Problems. In Fonseca, CM, Fleming, PJ, Zitzler, E, Deb, K, and Thiele, L (eds.), Evolutionary Multi-Criterion Optimization. Second International Conference, EMO 2003, pp. 737–751, Faro, Portugal, April 2003. Springer. Lecture Notes in Computer Science. Volume 2632.

    Google Scholar 

  87. Ramos, RM, Saldanha, RR, Takahashi, RHC, and Moreira, FJS, The Real-Biased Multiobjective Genetic Algorithm and Its Application to the Design of Wire Antennas. IEEE Transactions on Magnetics, 39(3):1329–1332, May 2003.

    Article  Google Scholar 

  88. Sbalzarini, IF, Müller, S, and Koumoutsakos, P, Microchannel Optimization Using Multiobjective Evolution Strategies. In Zitzler, E, Deb, K, Thiele, L, Coello Coello, CA, and Corne, D (eds.), First International Conference on Evolutionary Multi-Criterion Optimization, pp. 516–530. Springer-Verlag. Lecture Notes in Computer Science No. 1993, 2001.

    Google Scholar 

  89. Ishibuchi, H, Yoshida, T, and Murata, T, Balance Between Genetic Search and Local Search in Memetic Algorithms for Multiobjective Permutation Flowshop Scheduling. IEEE Transactions on Evolutionary Computation, 7(2):204–223, April 2003.

    Article  Google Scholar 

  90. Talbi, E-G, Rahoual, M, Mabed, MH, and Dhaenens, C, A Hybrid Evolutionary Approach for Multicriteria Optimization Problems: Application to the Flow Shop. In Zitzler, E, Deb, K, Thiele, L, Coello Coello, CA, and Corne, D (eds.), First International Conference on Evolutionary Multi-Criterion Optimization, pp. 416–428. Springer-Verlag. Lecture Notes in Computer Science No. 1993, 2001.

    Google Scholar 

  91. Brizuela, C, Sannomiya, N, and Zhao, Y, Multi-Objective Flow-Shop: Preliminary Results. In Zitzler, E, Deb, K, Thiele, L, Coello Coello, CA, and Corne, D (eds.), First International Conference on Evolutionary Multi-Criterion Optimization, pp. 443–457. Springer-Verlag. Lecture Notes in Computer Science No. 1993, 2001.

    Google Scholar 

  92. Jaszkiewicz, A, Hapke, M, and Kominek, P, Performance of Multiple Objective Evolutionary Algorithms on a Distribution System Design Problem—Computational Experiment. In Zitzler, E, Deb, K, Thiele, L, Coello Coello, CA, and Corne, D (eds.), First International Conference on Evolutionary Multi-Criterion Optimization, pp. 241–255. Springer-Verlag. Lecture Notes in Computer Science No. 1993, 2001.

    Google Scholar 

  93. Krause, M and Nissen, V, On using penalty functions and multicriteria optimisation techniques in facility layout. In Biethahn, J and Nissen, V (eds.), Evolutionary Algorithms in Management Applications. Springer-Verlag, Berlin, Germany, 1995.

    Google Scholar 

  94. Ducheyne, EI, De Wulf, RR, and De Baets, B, Bi-objective genetic algorithm for forest management: a comparative study. In Proceedings of the 2001 Genetic and Evolutionary Computation Conference. Late-Breaking Papers, pp. 63–66, San Francisco, CA, July 2001.

    Google Scholar 

  95. Jones, G, Brown, RD, Clark, DE, Willett, P, and Glen, RC, Searching databases of two-dimensional and three-dimensional chemical structures using genetic algorithms. In Forrest, S (ed), Proceedings of the Fifth International Conference on Genetic Algorithms, pp. 597–602, San Mateo, California, 1993. Morgan Kaufmann.

    Google Scholar 

  96. Hinchliffe, M, Willis, M, and Tham, M, Chemical process systems modelling using multi-objective genetic programming. In Koza, JR, Banzhaf, W, Chellapilla, K, Deb, K, Dorigo, M, Fogel, DB, Garzon, MH, Goldberg, DE, Iba, H, and Riolo, RL (eds.), Proceedings of the Third Annual Conference on Genetic Programming, pp. 134–139, San Mateo, CA, July 1998. Morgan Kaufmann Publishers.

    Google Scholar 

  97. Kunha, A, Oliveira, P, and Covas, JA, Genetic algorithms in multiobjective optimization problems: An application to polymer extrusion. In Wu, AS (ed), Proceedings of the 1999 Genetic and Evolutionary Computation Conference. Workshop Program, pp. 129–130, Orlando, FL, July 1999.

    Google Scholar 

  98. Parks, GT, Multiobjective pressurized water reactor reload core design by nondominated genetic algorithm search. Nuclear Science and Engineering, 124(1):178–187, 1996.

    Google Scholar 

  99. Golovkin, I, Mancini, R, Louis, S, Ochi, Y, Fujita, K, Nishimura, H, Shirga, H, Miyanaga, N, Azechi, H, Butzbach, R, Uschmann, I, Förster, E, Delettrez, J, Koch, J, Lee, RW, and Klein, L, Spectroscopic Determination of Dynamic Plasma Gradients in Implosion Cores. Physical Review Letters, 88(4), January 2002.

    Google Scholar 

  100. de Toro, F, Ros, E, Mota, S, and Ortega, J, Non-invasive Atrial Disease Diagnosis Using Decision Rules: A Multi-objective Optimization Approach. In Fonseca, CM, Fleming, PJ, Zitzler, E, Deb, K, and Thiele, L (eds.), Evolutionary Multi-Criterion Optimization. Second International Conference, EMO 2003, pp. 638–647, Faro, Portugal, April 2003. Springer. Lecture Notes in Computer Science. Volume 2632.

    Google Scholar 

  101. Aguilar, J and Miranda, P, Approaches Based on Genetic Algorithms for Multiobjective Optimization Problems. In Banzhaf, W, Daida, J, Eiben, AE, Garzon, MH, Honavar, V, Jakiela, M, and Smith, RE (eds.), Proceedings of the Genetic and Evolutionary Computation Conference (GECCO’99), volume 1, pp. 3–10, Orlando, Florida, USA, 1999. Morgan Kaufmann Publishers.

    Google Scholar 

  102. Lahanas, M, Schreibmann, E, Milickovic, N, and Baltas, D, Intensity Modulated Beam Radiation Therapy Dose Optimization with Multiobjective Evolutionary Algorithms. In Fonseca, CM, Fleming, PJ, Zitzler, E, Deb, K, and Thiele, L (eds.), Evolutionary Multi-Criterion Optimization. Second International Conference, EMO 2003, pp. 648–661, Faro, Portugal, April 2003. Springer. Lecture Notes in Computer Science. Volume 2632.

    Google Scholar 

  103. Dasgupta, D and González, FA, Evolving Complex Fuzzy Classifier Rules Using a Linear Tree Genetic Representation. In Spector, L, Goodman, ED, Wu, A, Langdon, WB, Voigt, H-M, Gen, M, Sen, S, Dorigo, M, Pezeshk, S, Garzon, MH, and Burke, E (eds.), Proceedings of the Genetic and Evolutionary Computation Conference (GECCO’2001), pp. 299–305, San Francisco, California, 2001. Morgan Kaufmann Publishers.

    Google Scholar 

  104. Fornaciari, W, Micheli, P, Salice, F, and Zampella, L, A First Step Towards Hw/Sw Partitioning of UML Specifications. In IEEE/ACM Design Automation and Test in Europe (DATE’03), pp. 668–673, Munich, Germany, March 2003. IEEE.

    Google Scholar 

  105. Ekárt, A and Németh, SZ, Selection Based on the Pareto Nondomination Criterion for Controlling Code Growth in Genetic Programming. Genetic Programming and Evolvable Machines, 2(1):61–73, March 2001.

    Article  Google Scholar 

  106. Llorà, X and Goldberg, DE, Bounding the Effect of Noise in Multiobjective Learning Classifier Systems. Evolutionary Computation, 11(3):279–298, Fall 2003.

    Google Scholar 

  107. Deb, K, Multi-Objective Genetic Algorithms: Problem Difficulties and Construction of Test Problems. Evolutionary Computation, 7(3):205–230, Fall 1999.

    PubMed  Google Scholar 

  108. Van Veldhuizen, DA and Lamont, GB, Multiobjective optimization with messy genetic algorithms. In Proceedings of the 2000 ACM Symposium on Applied Computing, pp. 470–476, Villa Olmo, Como, Italy, 2000. ACM.

    Google Scholar 

  109. Deb, K, Pratap, A, and Meyarivan, T, Constrained test problems for multi-objective evolutionary optimization. In Zitzler, E, Deb, K, Thiele, L, Coello Coello, CA, and Corne, D (eds.), First International Conference on Evolutionary Multi-Criterion Optimization, pp. 284–298. Springer-Verlag. Lecture Notes in Computer Science No. 1993, Berlin, Germany, 2001.

    Google Scholar 

  110. Deb, K, Thiele, L, Laumanns, M, and Zitzler, E, Scalable Multi-Objective Optimization Test Problems. In Congress on Evolutionary Computation (CEC’2002), volume 1, pp. 825–830, Piscataway, New Jersey, May 2002. IEEE Service Center.

    Article  Google Scholar 

  111. Parmee, IC, Poor-Definition, Uncertainty, and Human Factors—Satisfying Multiple Objectives in Real-World Decision-Making Environments. In Zitzler, E, Deb, K, Thiele, L, Coello Coello, CA, and Corne, D (eds.), First International Conference on Evolutionary Multi-Criterion Optimization, pp. 67–81. Springer-Verlag. Lecture Notes in Computer Science No. 1993, 2001.

    Google Scholar 

  112. Tiwari, A, Roy, R, Jared, G, and Munaux, O, Interaction and Multi-Objective Optimisation. In Spector, L, Goodman, ED, Wu, A, Langdon, WB, Voigt, H-M, Gen, M, Sen, S, Dorigo, M, Pezeshk, S, Garzon, MH, and Burke, E (eds.), Proceedings of the Genetic and Evolutionary Computation Conference (GECCO’2001), pp. 671–678, San Francisco, California, 2001. Morgan Kaufmann Publishers.

    Google Scholar 

  113. Hughes, EJ, Constraint Handling With Uncertain and Noisy Multi-Objective Evolution. In Proceedings of the Congress on Evolutionary Computation 2001 (CEC’2001), volume 2, pp. 963–970, Piscataway, New Jersey, May 2001. IEEE Service Center.

    Article  Google Scholar 

  114. Zitzler, E, Deb, K, and Thiele, L, Comparison of Multiobjective Evolutionary Algorithms: Empirical Results. Evolutionary Computation, 8(2):173–195, Summer 2000.

    Article  PubMed  Google Scholar 

  115. Van Veldhuizen, DA, Multiobjective Evolutionary Algorithms: Classifications, Analyses, and New Innovations. PhD thesis, Department of Electrical and Computer Engineering. Graduate School of Engineering. Air Force Institute of Technology, Wright-Patterson AFB, OH, May 1999.

    Google Scholar 

  116. Van Veldhuizen, DA and Lamont, GB, Multiobjective evolutionary algorithm research: A history and analysis. Technical Report TR-98-03, Department of Electrical and Computer Engineering, Graduate School of Engineering, Air Force Institute of Technology, Wright-Patterson AFB, OH, 1998.

    Google Scholar 

  117. Schott, JR, Fault tolerant design using single and multicriteria genetic algorithm optimization. Master’s thesis, Department of Aeronautics and Astronautics, Massachusetts Institute of Technology, Cambridge, MA, May 1995.

    Google Scholar 

  118. Van Veldhuizen, DA and Lamont, GB, Multiobjective evolutionary algorithm test suites. In Carroll, J, Haddad, H, Oppenheim, D, Bryant, B, and Lamont, GB (eds.), Proceedings of the 1999 ACM Symposium on Applied Computing, pp. 351–357, San Antonio, TX, 1999. ACM.

    Google Scholar 

  119. Zitzler, E, Laumanns, M, Thiele, L, Fonseca, CM, and Grunert da Fonseca, V, Why Quality Assessment of Multiobjective Optimizers Is Difficult. In Langdon, WB, Cantú-Paz, E, Mathias, K, Roy, R, Davis, D, Poli, R, Balakrishnan, K, Honavar, V, Rudolph, G, Wegener, J, Bull, L, Potter, MA, Schultz, AC, Miller, JF, Burke, E, and Jonoska, N (eds.), Proceedings of the Genetic and Evolutionary Computation Conference (GECCO’2002), pp. 666–673, San Francisco, California, July 2002. Morgan Kaufmann Publishers.

    Google Scholar 

  120. Knowles, J and Corne, D, On Metrics for Comparing Nondominated Sets. In Congress on Evolutionary Computation (CEC’2002), volume 1, pp. 711–716, Piscataway, New Jersey, May 2002. IEEE Service Center.

    Article  Google Scholar 

  121. Zitzler, E, Thiele, L, Laumanns, M, Fonseca, CM, and Grunert da Fonseca, V, Performance Assessment of Multiobjective Optimizers: An Analysis and Review. IEEE Transactions on Evolutionary Computation, 7(2):117–132, April 2003.

    Article  Google Scholar 

  122. Rudolph, G, On a Multi-Objective Evolutionary Algorithm and Its Convergence to the Pareto Set. In Proceedings of the 5th IEEE Conference on Evolutionary Computation, pp. 511–516, Piscataway, NJ, 1998. IEEE Press.

    Google Scholar 

  123. Rudolph, G and Agapie, A, Convergence Properties of Some Multi-Objective Evolutionary Algorithms. In Proceedings of the 2000 Conference on Evolutionary Computation, volume 2, pp. 1010–1016, Piscataway, NJ, July 2000. IEEE Press.

    Google Scholar 

  124. Hanne, T, On the convergence of multiobjective evolutionary algorithms. European Journal of Operational Research, 117(3):553–564, September 2000.

    Article  Google Scholar 

  125. Hanne, T, Global multiobjective optimization using evolutionary algorithms. Journal of Heuristics, 6(3):347–360, August 2000.

    Article  Google Scholar 

  126. Van Veldhuizen, DA and Lamont, GB, Evolutionary computation and convergence to a pareto front. In Koza, JR (ed), Late Breaking Papers at the Genetic Programming 1998 Conference, pp. 221–228, Stanford, CA, July 1998. Stanford University Bookstore.

    Google Scholar 

  127. Laumanns, M, Thiele, L, Zitzler, E, and Deb, K, Archiving with Guaranteed Convergence and Diversity in Multi-Objective Optimization. In Langdon, WB, Cantú-Paz, E, Mathias, K, Roy, R, Davis, D, Poli, R, Balakrishnan, K, Honavar, V, Rudolph, G, Wegener, J, Bull, L, Potter, MA, Schultz, AC, Miller, JF, Burke, E, and Jonoska, N (eds.), Proceedings of the Genetic and Evolutionary Computation Conference (GECCO’2002), pp. 439–447, San Francisco, California, July 2002. Morgan Kaufmann Publishers.

    Google Scholar 

  128. Wright, S, The roles of mutation, inbreeding, crossbreeding and selection in evolution. In Jones, DF (ed), Proceedings of the Sixth International Conference on Genetics, volume 1, pp. 356–366, 1932.

    Google Scholar 

  129. Altenberg, L, NK fitness landscapes. In Bäck, T, Fogel, DB, and Michalewicz, Z (eds.), Handbook of Evolutionary Computation, chapter B2.7.2, pp. B2.7:5–B2.7:10. Oxford University Press, New York, NY, 1997.

    Google Scholar 

  130. Laumanns, M, Thiele, L, Zitzler, E, Welzl, E, and Deb, K, Running Time Analysis of Multi-objective Evolutionary Algorithms on a Simple Discrete Optimization Problem. In Merelo Guervós, JJ, Adamidis, P, Beyer, HG, Fernández-Villacañas, JL, and Schwefel, H-P (eds.), Parallel Problem Solving from Nature—PPSN VII, pp. 44–53, Granada, Spain, September 2002. Springer. Lecture Notes in Computer Science No. 2439.

    Google Scholar 

  131. Laumanns, M, Thiele, L, Deb, K, and Zitzler, E, Combining Convergence and Diversity in Evolutionary Multi-objective Optimization. Evolutionary Computation, 10(3):263–282, Fall 2002.

    Google Scholar 

  132. Coello Coello, CA, Handling Preferences in Evolutionary Multiobjective Optimization: A Survey. In 2000 Congress on Evolutionary Computation, volume 1, pp. 30–37, Piscataway, New Jersey, July 2000. IEEE Service Center.

    Article  Google Scholar 

  133. Cvetković, D and Parmee, IC, Preferences and their Application in Evolutionary Multiobjective Optimisation. IEEE Transactions on Evolutionary Computation, 6(1):42–57, February 2002.

    Article  Google Scholar 

  134. Farina, M, Deb, K, and Amato, P Dynamic Multiobjective Optimization Problems: Test Cases, Approximation, and Applications. In Fonseca, CM, Fleming, PJ, Zitzler, E, Deb, K, and Thiele, L (eds.), Evolutionary Multi-Criterion Optimization. Second International Conference, EMO 2003, pp. 311–326, Faro, Portugal, April 2003. Springer. Lecture Notes in Computer Science. Volume 2632.

    Google Scholar 

  135. Van Veldhuizen, DA, Zydallis, JB, and Lamont, GB, Considerations in Engineering Parallel Multiobjective Evolutionary Algorithms. IEEE Transactions on Evolutionary Computation, 7(2):144–173, April 2003.

    Article  Google Scholar 

Download references

Authors
  1. Carlos A. Coello Coello
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Department of Computer Science, Oklahoma State University, USA

    Ajith Abraham

  2. KES Center, University of South Australia, Australia

    Lakhmi Jain

  3. Department of Computer Science, Queens College, New York, USA

    Robert Goldberg

Rights and permissions

Reprints and permissions

Copyright information

© 2005 Springer-Verlag London Limited

About this chapter

Cite this chapter

Coello Coello, C.A. (2005). Recent Trends in Evolutionary Multiobjective Optimization. In: Abraham, A., Jain, L., Goldberg, R. (eds) Evolutionary Multiobjective Optimization. Advanced Information and Knowledge Processing. Springer, London. https://doi.org/10.1007/1-84628-137-7_2

Download citation

  • DOI: https://doi.org/10.1007/1-84628-137-7_2

  • Publisher Name: Springer, London

  • Print ISBN: 978-1-85233-787-2

  • Online ISBN: 978-1-84628-137-2

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation