Skip to main content

Evolutionary Multi-Objective Optimization: A Critical Review

  • Chapter
Evolutionary Optimization

Part of the book series: International Series in Operations Research & Management Science ((ISOR,volume 48))

Abstract

In this chapter, we will review some of the most representative research in the field of evolutionary multiobjective optimization. We will discuss the historical roots of multiobjective optimization, the motivation to use evolutionary algorithms, and the most popular techniques currently in use. Then, we will discuss some of the research currently under way, including our own. At the end, we will provide what we consider to be some of the most promising paths of future research.

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

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 169.99
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

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  • Arrow, K. J., Barankin, E. W., and Blackwell, D. (1953). Admissible Points of Convex Sets. In Kuhn, H. W. and Tucker, A. W., editors, Contributions to the Theory of Games, pages 87–91. Princeton University Press, Princeton, New Jersey.

    Google Scholar 

  • Belegundu, A. D., Murthy, D. V., Salagame, R. R., and Constants, E. W. (1994). Multiobjective Optimization of Laminated Ceramic Composites Using Genetic Algorithms. In Fifth AIAA/USAF/NASA Symposium on Multidisciplinary Analysis and Optimization, pages 1015–1022, Panama City, Florida. AIAA. Paper 84-4363-CP.

    Google Scholar 

  • Bentley, P. J. and Wakefield, J. P. (1997). Finding Acceptable Solutions in the Pareto-Optimal Range using Multiobjective Genetic Algorithms. In Chawdhry, P. K., Roy, R., and Pant, R. K., editors, Soft Computing in Engineering Design and Manufacturing, Part 5, pages 231–240, London. Springer Verlag London Limited. (Presented at the 2nd On-line World Conference on Soft Computing in Design and Manufacturing (WSC2)).

    Google Scholar 

  • Blickle, T., Teich, J., and Thiele, L. (1996). System-level synthesis using evolutionary algorithms. Technical Report TIK Report-Nr. 16, Computer Engineering and Communication Networks Lab (TIK), Swiss Federal Institute of Technology (ETH), Gloriastrasse 35, 8092 Zurich.

    Google Scholar 

  • Brans, J. P., Vincke, P., and Mareschal, B. (1986). How to select and how to rank projects: the PROMETHEE method. European Journal of Operational Research, 24(2):228–238.

    Article  MathSciNet  Google Scholar 

  • Camponogara, E. and Talukdar, S. N. (1997). A Genetic Algorithm for Constrained and Multiobjective Optimization. In Alander, J. T., editor, 3rd Nordic Workshop on Genetic Algorithms and Their Applications (3NWGA), pages 49–62, Vaasa, Finland. University of Vaasa.

    Google Scholar 

  • Chankong, V. and Haimes, Y. Y. (1983). Multiobjective Decision Making: Theory and Methodology. Systems Science and Engineering. North-Holland.

    Google Scholar 

  • Chipperfield, A. J. and Fleming, P. J. (1995). Gas Turbine Engine Controller Design using Multiobjective Genetic Algorithms. In Zalzala, A. M. S., editor, Proceedings of the First IEE/IEEE International Conference on Genetic Algorithms in Engineering Systems: Innovations and Applications, GALESIA’ 95, pages 214–219, Halifax Hall, University of Sheffield, UK. IEEE.

    Chapter  Google Scholar 

  • Coello, C. A., Christiansen, A. D., and Aguirre, A. H. (2000a). Use of Evolutionary Techniques to Automate the Design of Combinational Circuits. International Journal of Smart Engineering System Design, 2(4):299–314.

    Google Scholar 

  • Coello, C. A. C. (1996). An Empirical Study of Evolutionary Techniques for Multiobjective Optimization in Engineering Design. PhD thesis, Department of Computer Science, Tulane University, New Orleans, LA.

    Google Scholar 

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

    Google Scholar 

  • Coello, C. A. C. (2000a). Constraint-handling using an evolutionary multiobjective optimization technique. Civil Engineering Systems, 17:319–346.

    Article  Google Scholar 

  • Coello, C. A. C. (2000b). Handling Preferences in Evolutionary Multiobjective Optimization: A Survey. In 2000 Congress on Evolutionary Computation, volume 1, pages 30–37, Piscataway, New Jersey. IEEE Service Center.

    Google Scholar 

  • Coello, C. A. C. (2000c). Treating Constraints as Objectives for Single-Objective Evolutionary Optimization. Engineering Optimization, 32(3):275–308.

    Article  Google Scholar 

  • Coello, C. A. C., Aguirre, A. H., and Buckles, B. P. (2000b). Evolutionary Multiobjective Design of Combinational Logic Circuits. In Lohn, J., Stoica, A., Keymeulen, D., and Colombano, S., editors, Proceedings of the Second NASA/DoD Workshop on Evolvable Hardware, pages 161–170, Los Alamitos, California. IEEE Computer Society.

    Chapter  Google Scholar 

  • Coello, C. A. C. and Christiansen, A. D. (1998). Two New GA-based methods for multiobjective optimization. Civil Engineering Systems, 15(3):207–243.

    Article  Google Scholar 

  • Coello, C. A. C., Christiansen, A. D., and Aguirre, A. H. (1998). Using a New GA-Based Multiobjective Optimization Technique for the Design of Robot Arms. Robotica, 16(4):401–414.

    Article  Google Scholar 

  • Coello, C. A. C. and Toscano, G. (2001). A Micro-Genetic Algorithm for Multiobjective Optimization. In Zitzler, E., Deb, K., Thiele, L., Coello, C. A. C., and Corne, D., editors, First International Conference on Evolutionary Multi-Criterion Optimization, pages 127–141. Springer-Verlag. Lecture Notes in Computer Science No. 1993.

    Google Scholar 

  • Cohon, J. L. (1978). Multiobjective Programming and Planning. Academic Press.

    Google Scholar 

  • Cohon, J. L. and Marks, D. H. (1975). A Review and Evaluation of Multiobjective Programming Techniques. Water Resources Research, 11(2):208–220.

    Article  Google Scholar 

  • Cvetković, D. and Parmee, I. C. (2000). Designer’s preferences and multiobjective preliminary design processes. In Parmee, I. C., editor, Proceedings of the Fourth International Conference on Adaptive Computing in Design and Manufacture (ACDM’2000), pages 249–260. PEDC, University of Plymouth, UK, Springer London.

    Google Scholar 

  • Czyzak, P. and Jaszkiewicz, A. (1997). Pareto Simulated Annealing. In Fandel, G. and Gal, T., editors, Multiple Criteria Decision Making. Proceedings of the Xllth International Conference, pages 297–307, Hagen, Germany. Springer-Verlag.

    Google Scholar 

  • Das, I. and Dennis, J. (1997). 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.

    Article  Google Scholar 

  • Deb, K. (1999a). Multi-Objective Evolutionary Algorithms: Introducing Bias Among Pareto-Optimal Solutions. KanGAL report 99002, Indian Institute of Technology, Kanpur, India.

    Google Scholar 

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

    Article  CAS  PubMed  MathSciNet  Google Scholar 

  • Deb, K. (1999c). Solving Goal Programming Problems Using Multi-Objective Genetic Algorithms. In 1999 Congress on Evolutionary Computation, pages 77–84, Washington, D.C. IEEE Service Center.

    Google Scholar 

  • Deb, K. (2001). An Efficient Constraint Handling Method for Genetic Algorithms. Computer Methods in Applied Mechanics and Engineering. (in Press).

    Google Scholar 

  • Deb, K., Agrawal, S., Pratab, A., and Meyarivan, T. (2000a). A Fast Elitist Non-Dominated Sorting Genetic Algorithm for Multi-Objective Optimization: NSGA-II. KanGAL report 200001, Indian Institute of Technology, Kanpur, India.

    Google Scholar 

  • Deb, K., Agrawal, S., Pratab, A., and Meyarivan, T. (2000b). A Fast Elitist Non-Dominated Sorting Genetic Algorithm for Multi-Objective Optimization: NSGA-II. In Proceedings of the Parallel Problem Solving from Nature VI Conference, pages 849–858. Springer.

    Google Scholar 

  • Deb, K. and Goldberg, D. E. (1989). An Investigation of Niche and Species Formation in Genetic Function Optimization. In Schaffer, J. D., editor, Proceedings of the Third International Conference on Genetic Algorithms, pages 42–50, San Mateo, California. George Mason University, Morgan Kaufmann Publishers.

    Google Scholar 

  • Deb, K. and Meyarivan, T. (2000). Constrained Test Problems for Multi-Objective Evolutionary Optimization. KanGAL report 200005, Indian Institute of Technology, Kanpur, India.

    Google Scholar 

  • Dick, R. P. and Jha, N. K. (1998). MOGAC: A Multiobjective Genetic Algorithm for Hardware-Software Co-synthesis of Hierarchical Heterogeneous Distributed Embedded Systems. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 17(10):920–935.

    Article  Google Scholar 

  • Duckstein, L. (1984). Multiobjective Optimization in Structural Design: The Model Choice Problem. In Atrek, E., Gallagher, R. H., Ragsdell, K. M., and Zienkiewicz, O. C., editors, New Directions in Optimum Structural Design, pages 459–481. John Wiley and Sons.

    Google Scholar 

  • Edgeworth, F. Y. (1881). Mathematical Physics. P. Keagan, London, England.

    Google Scholar 

  • Ehrgott, M. (2000). Approximation algorithms for combinatorial multicriteria optimization problems. International Transactions in Operational Research, 7:5–31.

    Article  MathSciNet  Google Scholar 

  • Ehrgott, M. and Gandibleux, X. (2000). An Annotated Bibliography of Multi-objective Combinatorial Optimization. Technical Rep.-62/2000, Fachbereich Mathematik, Universitat Kaiserslautern, Kaiserslautern, Germany.

    Google Scholar 

  • Fonseca, C. M. and Fleming, P. J. (1993). Genetic Algorithms for Multiobjective Optimization: Formulation, Discussion and Generalization. In Forrest, S., editor, Proceedings of the Fifth International Conference on Genetic Algorithms, pages 416–423, San Mateo, California. University of Illinois at Urbana-Champaign, Morgan Kauffman Publishers.

    Google Scholar 

  • Fonseca, C. M. and Fleming, P. J. (1996). On the Performance Assessment and Comparison of Stochastic Multiobjective Optimizers. In Voigt, H.-M., Ebeling, W., Rechenberg, I., and Schwefel, H.-P., editors, Parallel Problem Solving from Nature—PPSNIV, Lecture Notes in Computer Science, pages 584–593, Berlin, Germany. Springer-Verlag.

    Google Scholar 

  • Fonseca, C. M. and Fleming, P. J. (1998). Multiobjective Optimization and Multiple Constraint Handling with Evolutionary Algorithms—Part I: A Unified Formulation. IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans, 28(1):26–37.

    Article  Google Scholar 

  • Gandibleux, X., Mezdaoui, N., and Fréville, N. (1997). A tabu search procedure to solve combinatorial optimisation problems. In Caballero, R., Ruiz, F., and Steuer, R., editors, Advances in Multiple Objective and Goal Programming, volume 455 of Lecture Notes in Economics and Mathematical Systems, pages 291–300. Springer-Verlag.

    Google Scholar 

  • Gen, M. and Cheng, R. (1997). Genetic Algorithms and Engineering Design. John Wiley and Sons, Inc., New York.

    Google Scholar 

  • Goicoechea, A., Duckstein, L., and Fogel, M. (1979). Multiple objectives under uncertainty: An illustrative application of PROTRADE. Water Resources Research, 15(2):203–210.

    Article  Google Scholar 

  • Goldberg, D. E. (1989). Genetic Algorithms in Search, Optimization and Machine Learning. Addison-Wesley Publishing Company, Reading, Massachusetts.

    Google Scholar 

  • Greenwood, G. W., Hu, X. S., and D’Ambrosio, J. G. (1997). Fitness Functions for Multiple Objective Optimization Problems: Combining Preferences with Pareto Rankings. In Belew, R. K. and Vose, M. D., editors, Foundations of Genetic Algorithms 4, pages 437–455, San Mateo, California. Morgan Kaufmann.

    Google Scholar 

  • Habenicht, W. (1982). Quad trees, A data structure for discrete vector optimization problems. In Lecture notes in economics and mathematical systems, volume 209, pages 136–145.

    Google Scholar 

  • Hajela, P. and Lin, C. Y. (1992). Genetic search strategies in multicriterion optimal design. Structural Optimization, 4:99–107.

    Article  ADS  Google Scholar 

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

    Article  MathSciNet  Google Scholar 

  • Hansen, M. P. (1996). Tabu Search in Multiobjective Optimisation: MOTS. In Proceedings of MCDM’97, Cape Town, South Africa.

    Google Scholar 

  • Hartmann, J. W., Coverstone-CarrolI, V., and Williams, S. N. (1998). Optimal interplanetary spacecraft trajectories via a pareto genetic algorithm. Journal of the Astronautical Sciences, 46(3):267–282.

    MathSciNet  Google Scholar 

  • Hilliard, M. R., Liepins, G. E., Palmer, M., and Rangarajen, G. (1989). The computer as a partner in algorithmic design: Automated discovery of parameters for a multiobjective scheduling heuristic. In Sharda, R., Golden, B. L., Wasil, E., Balci, O., and Stewart, W., editors, Impacts of Recent Computer Advances on Operations Research. North-Holland Publishing Company, New York.

    Google Scholar 

  • Hinterding, R. and Michalewicz, Z. (1998). Your Brains and My Beauty: Parent Matching for Constrained Optimisation. In Proceedings of the 5th International Conference on Evolutionary Computation, pages 810–815, Anchorage, Alaska.

    Google Scholar 

  • Horn, J. (1997). Multicriterion Decision Making. In Bäck, T., Fogel, D., and Michalewicz, Z., editors, Handbook of Evolutionary Computation, volume 1, pages F1.9:1–F1.9:15. IOP Publishing Ltd. and Oxford University Press.

    Google Scholar 

  • Horn, J., Nafpliotis, N., and Goldberg, D. E. (1994). 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, pages 82–87, Piscataway, New Jersey. IEEE Service Center.

    Chapter  Google Scholar 

  • Hwang, C. L. and Masud, A. S. M. (1979). Multiple Objective Decision-Making Methods and Applications. In Lecture Notes in Economics and Mathematical Systems, volume 164. Springer-Verlag, New York.

    Google Scholar 

  • Hwang, C. L., Paidy, S. R., and Yoon, K. (1980). Mathematical Programming with Multiple Objectives: A Tutorial. Computing and Operational Research, 7:5–31.

    Article  Google Scholar 

  • Ishibuchi, H. and Murata, T. (1996). Multi-Objective Genetic Local Search Algorithm. In Fukuda, T. and Furuhashi, T., editors, Proceedings of the 1996 International Conference on Evolutionary Computation, pages 119–124, Nagoya, Japan. IEEE.

    Chapter  Google Scholar 

  • Jakob, W., Gorges-Schleuter, M., and Blume, C. (1992). Application of Genetic Algorithms to task planning and learning. In Männer, R. and Manderick, B., editors, Parallel Problem Solving from Nature, 2nd Workshop, Lecture Notes in Computer Science, pages 291–300, Amsterdam. North-Holland Publishing Company.

    Google Scholar 

  • Jaszkiewicz, A. (2000). On the performance of multiple objective genetic local search on the 0/1 knapsack problem, a comparative experiment. Technical Report RA-002/2000, Institute of Computing Science, Poznan University of Technology, PoznaÅ„a, Poland.

    Google Scholar 

  • Jiménez, F., Verdegay, J. L., and Gómez-Skarmeta, A. F. (1999). Evolutionary Techniques for Constrained Multiobjective Optimization Problems. In Wu, A. S., editor, Proceedings of the 1999 Genetic and Evolutionary Computation Conference. Workshop Program, pages 115–116, Orlando, Florida.

    Google Scholar 

  • Karlin, S. (1959). Mathematical Methods and Theory in Games. In Programming and Economics, volume 1, pages 216–217. Addison-Wesley, Reading, Massachusetts.

    Google Scholar 

  • Knowles, J. D. and Corne, D. W. (1999). The Pareto Archived Evolution Strategy: A New Baseline Algorithm for Multiobjective Optimisation. In 1999 Congress on Evolutionary Computation, pages 98–105, Washington, D.C. IEEE Service Center.

    Google Scholar 

  • Knowles, J. D. and Corne, D. W. (2000). Approximating the Nondominated Front Using the Pareto Archived Evolution Strategy. Evolutionary Computation, 8(2):149–172.

    Article  CAS  PubMed  Google Scholar 

  • Koopman, B. O. (1953). The Optimum Distribution of Effort. Operations Research, 1(2):52–63.

    Article  MathSciNet  Google Scholar 

  • Koopmans, T. C. (1951). Analysis of Production as an efficient combination of activities. In Koopmans, T. C., editor, Activity Analysis of Production and Allocation, Cowles Commision Monograph No. 13, pages 33–97. John Wiley and Sons, New York, New York.

    Google Scholar 

  • Krishnakumar, K. (1989). Micro-genetic algorithms for stationary and non-stationary function optimization. In SPIE Proceedings: Intelligent Control and Adaptive Systems, pages 289–296.

    Google Scholar 

  • Kuhn, H. W. and Tucker, A. W. (1951). Nonlinear programming. In Neyman, J., editor, Proceedings of the Second Berkeley Symposium on Mathematical Statistics and Probability, pages 481–492, Berkeley, California. University of California Press.

    Google Scholar 

  • Kurahashi, S. and Terano, T. (2000). A Genetic Algorithm with Tabu Search for Multimodal and Multiobjective Function Optimization. In Whitley, D., Goldberg, D., Cantú-Paz, E., Spector, L., Parmee, I, and Beyer, H.-G., editors, Proceedings of the Genetic and Evolutionary Computation Conference (GECCO’2000), pages 291–298, San Francisco, California. Morgan Kaufmann.

    Google Scholar 

  • Liu, X., Begg, D. W., and Fishwick, R. J. (1998). Genetic approach to optimal topology/controller design of adaptive structures. International Journal for Numerical Methods in Engineering, 41:815–830.

    Article  Google Scholar 

  • Loucks, D. P. (1975). Conflict and choice: Planning for multiple objectives. In Blitzer, C., Clark, P., and Taylor, L., editors, Economy wide Models and Development Planning, New York, New York. Oxford University Press.

    Google Scholar 

  • Marglin, S. (1967). Public Investment Criteria. MIT Press, Cambridge, Massachusetts.

    Google Scholar 

  • Massebeuf, S., Fonteix, C., Kiss, L. N., Marc, I., Pla, F., and Zaras, K. (1999). Multicriteria Optimization and Decision Engineering of an Extrusion Process Aided by a Diploid Genetic Algorithm. In 1999 Congress on Evolutionary Computation, pages 14–21, Washington, D.C. IEEE Service Center.

    Google Scholar 

  • Michalewicz, Z. (1996). Genetic Algorithms + Data Structures = Evolution Programs. Springer-Verlag, third edition.

    Google Scholar 

  • Miettinen, K. M. (1998). Nonlinear Multiobjective Optimization. Kluwer Academic Publishers, Boston, Massachusetts.

    Google Scholar 

  • Mitchell, M. (1996). An Introduction to Genetic Algorithms. MIT Press, Cambridge, Massachusetts.

    Google Scholar 

  • Mitra, K., Deb, K., and Gupta, S. K. (1998). Multiobjective Dynamic Optimization of an Industrial Nylon 6 Semibatch Reactor Using Genetic Algorithm. Journal of Applied Polymer Science, 69(1):69–87.

    Article  CAS  Google Scholar 

  • Monarchi, D. E., Kisiel, C. C., and Duckstein, L. (1973). Interactive Multiobjective Programming in Water Resources: A Case Study. Water Resources Research, 9(4):837–850.

    Article  Google Scholar 

  • Narayanan, S. and Azarm, S. (1999). On Improving Multiobjective Genetic Algorithms for Design Optimization. Structural Optimization, 18:146–155.

    Google Scholar 

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

    Google Scholar 

  • Parks, G. T. and Miller, I. (1998). Selective Breeding in a Multiobjective Genetic Algorithm. In Eiben, A. E., Schoenauer, M., and Schwefel, H.-P., editors, Parallel Problem Solving From Nature — PPSN V, pages 250–259, Amsterdam, Holland. Springer-Verlag.

    Chapter  Google Scholar 

  • Parmee, I. C. and Purchase, G. (1994). The development of a directed genetic search technique for heavily constrained design spaces. In Parmee, I. C., editor, Adaptive Computing in Engineering Design and Control-’94 pages 97–102, Plymouth, UK. University of Plymouth, University of Plymouth.

    Google Scholar 

  • Pierreval, H. and Plaquin, M.-F. (1998). An Evolutionary Approach of Multicriteria Manufacturing Cell Formation. International Transactions in Operational Research, 5(1):13–25.

    Article  Google Scholar 

  • Quagliarella, D. and Vicini, A. (1997). Coupling Genetic Algorithms and Gradient Based Optimization Techniques. In Quagliarella, D., Périaux, J., Poloni, C., and Winter, G., editors, Genetic Algorithms and Evolution Strategies in Engineering and Computer Science. Recent Advances and Industrial Applications, chapter 14, pages 289–309. John Wiley and Sons, West Sussex, England.

    Google Scholar 

  • Ray, T., Kang, T., and Chye, S. K. (2000). An Evolutionary Algorithm for Constrained Optimization. In Whitley, D., Goldberg, D., Cantú-Paz, E., Spector, L., Parmee, I., and Beyer, H.-G., editors, Proceedings of the Genetic and Evolutionary Computation Conference (GECCO’2000), pages 771–777, San Francisco, California. Morgan Kaufmann.

    Google Scholar 

  • Rekiek, B., Lit, P. D., Pellichero, F., L’Eglise, T., Falkenauer, E., and Delchambre, A. (2000). Dealing With User’s Preferences in Hybrid Assembly Lines Design. In Proceedings of the MCPL’2000 Conference.

    Google Scholar 

  • Richardson, J. T., Palmer, M. R., Liepins, G., and Hilliard, M. (1989). Some Guidelines for Genetic Algorithms with Penalty Functions. In Schaffer, J. D., editor, Proceedings of the Third International Conference on Genetic Algorithms, pages 191–197, George Mason University. Morgan Kaufmann Publishers.

    Google Scholar 

  • Ritzel, B. J., Eheart, J. W., and Ranjithan, S. (1994). Using genetic algorithms to solve a multiple objective groundwater pollution containment problem. Water Resources Research, 30(5): 1589–1603.

    Article  CAS  ADS  Google Scholar 

  • Romero, C. E. M. and Manzanares, E. M. (1999). MOAQ an Ant-Q Algorithm for Multiple Objective Optimization Problems. In Banzhaf, W., Daida, J., Eiben, A. E., Garzon, M. H., Honavar, V., Jakiela, M., and Smith, R. E., editors, Genetic and Evolutionary Computing Conference (GECCO 99), volume 1, pages 894–901, San Francisco, California. Morgan Kaufmann.

    Google Scholar 

  • Rosenberg, R. S. (1967). Simulation of genetic populations with biochemical properties. PhD thesis, University of Michigan, Ann Harbor, Michigan.

    Google Scholar 

  • Rudolph, G. (1998). On a Multi-Objective Evolutionary Algorithm and Its Convergence to the Pareto Set. In Proceedings of the 5th IEEE Conference on Evolutionary Computation, pages 511–516, Piscataway, New Jersey. IEEE Press.

    Google Scholar 

  • Rudolph, G. and Agapie, A. (2000). Convergence Properties of Some Multi-Objective Evolutionary Algorithms. In Proceedings of the 2000 Conference on Evolutionary Computation, volume 2, pages 1010–1016, Piscataway, New Jersey. IEEE Press.

    Google Scholar 

  • Sandgren, E. (1994). Multicriteria design optimization by goal programming. In Adeli, H., editor, Advances in Design Optimization, chapter 23, pages 225–265. Chapman & Hall, London.

    Google Scholar 

  • Schaffer, J. D. (1985). Multiple Objective Optimization with Vector Evaluated Genetic Algorithms. In Genetic Algorithms and their Applications: Proceedings of the First International Conference on Genetic Algorithms, pages 93–100. Lawrence Erlbaum.

    Google Scholar 

  • Schroder, P., Chipperfield, A. J., Fleming, P. J., and Grum, N. (1997). Multi-Objective Optimization of Distributed Active Magnetic Bearing Controllers. In Genetic Algorithms in Engineering Systems: Innovations and Applications, pages 13–18. IEE.

    Google Scholar 

  • Shaw, K. J. and Fleming, P. J. (1997). Including Real-Life Preferences in Genetic Algorithms to Improve Optimisation of Production Schedules. In Proceedings of the GALESIA’ 97, Glasgow, Scotland. IEE.

    Google Scholar 

  • Srinivas, N. and Deb, K. (1994). Multiobjective Optimization Using Nondominated Sorting in Genetic Algorithms. Evolutionary Computation, 2(3):221–248.

    Article  Google Scholar 

  • Stadler, W. (1975). Preference optimality and applications to Pareto optimality. In Leitmann, G. and Marzollo, A., editors, Multi-Criteria Decision Making, volume 211. Springer-Verlag, New York.

    Google Scholar 

  • Stadler, W. (1988). Fundamentals of multicriteria optimization. In Stadler, W., editor, Multicriteria Optimization in Engineering and the Sciences, pages 1–25. Plenum Press, New York.

    Google Scholar 

  • Surry, P. D. and Radcliffe, N. J. (1997). The COMOGA Method: Constrained Optimisation by Multiobjective Genetic Algorithms. Control and Cybernetics, 26(3).

    Google Scholar 

  • Surry, P. D., Radcliffe, N. J., and Boyd, I. D. (1995). A Multi-Objective Approach to Constrained Optimisation of Gas Supply Networks: The COMOGA Method. In Fogarty, T. C., editor, Evolutionary Computing. AISB Workshop. Selected Papers, Lecture Notes in Computer Science, pages 166–180, Sheffield, U.K. Springer-Verlag.

    Google Scholar 

  • Tan, K. C. and Li, Y. (1997). Multi-Objective Genetic Algorithm Based Time and Frequency Domain Design Unification of Linear Control Systems. Technical Report CSC-97007, Department of Electronics and Electrical Engineering, University of Glasglow, Glasglow, Scotland.

    Google Scholar 

  • Tanino, T., Tanaka, M., and Hojo, C. (1993). An interactive multicriteria decision making method by using a genetic algorithm. In Proceedings of 2nd International Conference on Systems Science and Systems Engineering, pages 381–386.

    Google Scholar 

  • van Huylenbroeck, G. (1995). The Conflict Analysis Method: bridging the gap between ELECTRE, PROMETHEE and ORESTE. European Journal of Operational Research, 82(3):490–502.

    Article  MATH  Google Scholar 

  • Vedarajan, G., Chan, L. C., and Goldberg, D. E. (1997). Investment Portfolio Optimization using Genetic Algorithms. In Koza, J. R., editor, Late Breaking Papers at the Genetic Programming 1997 Conference, pages 255–263, Stanford University, California. Stanford Bookstore.

    Google Scholar 

  • Veldhuizen, D. A. V. (1999). 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, Ohio.

    Google Scholar 

  • Veldhuizen, D. A. V. and Lamont, G. B. (1998). Evolutionary Computation and Convergence to a Pareto Front. In Koza, J. R., editor, Late Breaking Papers at the Genetic Programming 1998 Conference, pages 221–228, Stanford University, California. Stanford University Bookstore.

    Google Scholar 

  • Veldhuizen, D. A. V. and Lamont, G. B. (1999). Multiobjective Evolutionary Algorithm Test Suites. In Carroll, J., Haddad, H., Oppenheim, D., Bryant, B., and Lamont, G. B., editors, Proceedings of the 1999 ACM Symposium on Applied Computing, pages 351–357, San Antonio, Texas. ACM.

    Chapter  Google Scholar 

  • Vincke, P. (1995). Analysis of MCDA in Europe. European Journal of Operational Research, 25:160–168.

    Article  Google Scholar 

  • von Neumann, J. and Morgenstern, O. (1944). Theory of Games and Economic Behavior. Princeton University Press, Princeton, New Jersey.

    Google Scholar 

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

    Article  CAS  Google Scholar 

  • Wilson, P. B. and Macleod, M. D. (1993). Low implementation cost IIR digital filter design using genetic algorithms. In IEE/IEEE Workshop on Natural Algorithms in Signal Processing, pages 4/1–4/8, Chelmsford, U.K.

    Google Scholar 

  • Zadeh, L. A. (1963). Optimality and Nonscalar-Valued Performance Criteria. IEEE Transactions on Automatic Control, AC-8(1):59–60.

    Article  Google Scholar 

  • Zebulum, R. S., Pacheco, M. A., and Vellasco, M. (1998). A multiobjective optimisation methodology applied to the synthesis of lowpower operational amplifiers. In Cheuri, I. J. and dos Reis Filho, C. A., editors, Proceedings of the XIII International Conference in Microelectronics and Packaging, volume 1, pages 264–271, Curitiba, Brazil.

    Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  • Zitzler, E., Teich, J., and Bhattacharyya, S. S. (1999). Multidimensional Exploration of Software Implementations for DSP Algorithms. VLSI Signal Processing Systems. (To appear).

    Google Scholar 

  • Zitzler, E. and Thiele, L. (1999). Multiobjective Evolutionary Algorithms: A Comparative Case Study and the Strength Pareto Approach. IEEE Transactions on Evolutionary Computation, 3(4):257–271.

    Article  Google Scholar 

Download references

Authors

Rights and permissions

Reprints and permissions

Copyright information

© 2003 Kluwer Academic Publishers

About this chapter

Cite this chapter

Coello Coello, C.A. (2003). Evolutionary Multi-Objective Optimization: A Critical Review. In: Evolutionary Optimization. International Series in Operations Research & Management Science, vol 48. Springer, Boston, MA. https://doi.org/10.1007/0-306-48041-7_5

Download citation

  • DOI: https://doi.org/10.1007/0-306-48041-7_5

  • Publisher Name: Springer, Boston, MA

  • Print ISBN: 978-0-7923-7654-5

  • Online ISBN: 978-0-306-48041-6

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics