Article

Conflict, harmony, and independence: relationships in evolutionary multi-criterion optimisation

Authors:

Robin C. Purshouse,

Peter J. FlemingAuthors Info & Claims

EMO'03: Proceedings of the 2nd international conference on Evolutionary multi-criterion optimization

Pages 16 - 30

Published: 08 April 2003 Publication History

Abstract

This paper contributes a platform for the treatment of large numbers of criteria in evolutionary multi-criterion optimisation theory through consideration of the relationships between pairs of criteria. In a conflicting relationship, as performance in one criterion is improved, performance in the other is seen to deteriorate. If the relationship is harmonious, improvement in one criterion is rewarded with simultaneous improvement in the other. The criteria may be independent of each other, where adjustment to one never affects adjustment to the other. Increasing numbers of conflicting criteria pose a great challenge to obtaining a good representation of the global trade-off hypersurface, which can be countered using decision-maker preferences. Increasing numbers of harmonious criteria have no effect on convergence to the surface but difficulties may arise in achieving a good distribution. The identification of independence presents the opportunity for a divide-and-conquer strategy that can improve the quality of trade-off surface representations.

References

[1]

Coello, C.A.C., Van Veldhuizen, D.A., Lamont, G.B.: Evolutionary Algorithms for Solving Multi-Objective Problems. Kluwer Academic Publishers, New York Boston Dordrecht London Moscow (2002).

Digital Library

[2]

Deb, K., Thiele, L., Laumanns, M., Zitzler, E.: Scalable Multi-Objective Optimization Test Problems. Proceedings of the 2002 IEEE Congress on Evolutionary Computation (CEC 2002), Vol. 1. (2002) 825-830.

[3]

Purshouse, R.C., Fleming, P.J.: Why use Elitism and Sharing in a Multi-Objective Genetic Algorithm? Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2002). (2002) 520-527.

Digital Library

[4]

Deb, K.: Multi-Objective Optimization using Evolutionary Algorithms. Wiley, Chichester New York Weinheim Brisbane Singapore Toronto (2001).

Digital Library

[5]

Fonseca, C.M., Fleming, P.J.: Multiobjective optimization and multiple constraint handling with evolutionary algorithms (a) Part I: A unified formulation (b) Part II: Application example. IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans 28 (1998) 26-37 and 38-47.

Digital Library

[6]

Scott, D.W.: Multivariate Density Estimation: Theory, Practice, and Visualization. Wiley, New York Chichester Brisbane Toronto Singapore (1992).

[7]

Demartines, P., Hérault, J.: Curvilinear Component Analysis: a Self-Organising Neural Network for Non-linear Mapping of Data Sets. IEEE Transactions on Neural Networks 8 (1997) 148-154.

Digital Library

[8]

Jolliffe, I.T.: Principal Component Analysis. Springer, New York (1986).

[9]

Chernoff, H.: The Use of Faces to Represent Points in k-Dimensional Space Graphically. Journal of the American Statistical Association 68 (1973) 361-368.

[10]

Cleveland, W.S.: Visualizing Data. Hobart Press, Summit (1993).

Digital Library

[11]

Dakev, N.V., Whidborne, J.F., Chipperfield, A.J., Fleming P.J.: Evolutionary H-infinity design of an electromagnetic suspension control system for a maglev vehicle. Proceedings of the Institution of Mechanical Engineers Part I 211 (1997) 345-355.

[12]

Agrell, P.J.: On redundancy in multi criteria decision-making. European Journal of Operational Research 98 (1997) 571-586.

[13]

Carlsson, C., Fullér, R.: Multiple Criteria Decision Making: The Case for Interdependence. Computers & Operations Research 22 (1995) 251-260.

Digital Library

[14]

Gal, T., Hanne, T.: Consequences of dropping nonessential objectives for the application of MCDM methods. European Journal of Operational Research 119 (1999) 373-378.

[15]

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

Digital Library

[16]

Purshouse, R.C., Fleming, P.J.: An Adaptive Divide-and-Conquer Methodology for Evolutionary Multi-Criterion Optimisation. This volume. (2003).

[17]

Inselberg, A.: The Plane with Parallel Coordinates. The Visual Computer 1 (1985) 69-91.

[18]

Fonseca, C.M., Fleming, P.J.: Genetic algorithms for multiobjective optimization: Formulation, discussion and generalization. Proceedings of the Fifth International Conference on Genetic Algorithms. (1993) 416-423.

Digital Library

[19]

Wegman, E.J.: Hyperdimensional Data Analysis Using Parallel Coordinates. Journal of the American Statistical Association 85 (1990) 664-675.

[20]

Kendall, M.G.: A new measure of rank correlation. Biometrika 30 (1938) 81-93.

[21]

Lee, J., Kuo, J.-Y.: Fuzzy decision making through trade-off analysis between criteria. Journal of Information Sciences 107 (1998) 107-126.

Digital Library

[22]

Schroder, P.: Multivariable control of active magnetic bearings. Doctoral dissertation, University of Sheffield. (1998).

[23]

Bouyssou, D.: Building criteria: a prerequisite for MCDA. In: Bana e Costa, C.A. (ed.): Readings in Multiple Criteria Decision Aid. Springer-Verlag, Berlin Heidelberg New York (1990) 58-80.

Cited By

Guo XWang YWang X(2016)An objective reduction algorithm using representative Pareto solution search for many-objective optimization problemsSoft Computing - A Fusion of Foundations, Methodologies and Applications10.1007/s00500-015-1776-420:12(4881-4895)Online publication date: 1-Dec-2016
https://dl.acm.org/doi/10.1007/s00500-015-1776-4
Pinheiro RLanda-Silva DAtkin JEsparcia-Alcázar A(2015)Analysis of Objectives Relationships in Multiobjective Problems Using Trade-Off Region MapsProceedings of the 2015 Annual Conference on Genetic and Evolutionary Computation10.1145/2739480.2754721(735-742)Online publication date: 11-Jul-2015
https://dl.acm.org/doi/10.1145/2739480.2754721
Bandyopadhyay SMukherjee A(2015)An Algorithm for Many-Objective Optimization With Reduced Objective Computations: A Study in Differential EvolutionIEEE Transactions on Evolutionary Computation10.1109/TEVC.2014.233287819:3(400-413)Online publication date: 1-Jun-2015
https://dl.acm.org/doi/10.1109/TEVC.2014.2332878
Show More Cited By

Recommendations

Multiobjective Harmony Search Algorithm Proposals

Harmony Search metaheuristic is successfully used in several applications of science and engineering. However, its effectiveness in solving multiobjective optimization problems using the concepts of Pareto optimality, remains unproved. This paper ...
Harmony Search for Multi-objective Optimization
SBRN '12: Proceedings of the 2012 Brazilian Symposium on Neural Networks

This paper investigates the efficiency of Harmony Search based algorithms for solving multi-objetive problems. For this task, four variants of the Harmony Search algorithm were adapted in the Non-dominated Sorting Genetic Algorithm II (NSGA-II) ...
A Non-parametric Harmony-Based Objective Reduction Method for Many-Objective Optimization
SMC '13: Proceedings of the 2013 IEEE International Conference on Systems, Man, and Cybernetics

Multiobjective optimization has been applied successfully to real-world optimization problems with few objectives. However, the performance of current algorithms for multiobjective optimization reduces exponentially as the number of objectives grows. In ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image Guide Proceedings

EMO'03: Proceedings of the 2nd international conference on Evolutionary multi-criterion optimization

April 2003

811 pages

ISBN:3540018697

Editors:
Carlos M. Fonseca
University of the Algarve, Faculty of Sciences and Technology, Centre for Intelligent Systems, Faro, Portugal
,
Peter J. Fleming
University of Sheffield, Department of Automatic Control and Systems Engineering, Sheffield, UK
,
Eckart Zitzler
Swiss Federal Institute of Technology, Department of Electrical Engineering, Computer Engineering and Networks Laboratory, Zürich, Switzerland
,
Lothar Thiele
Swiss Federal Institute of Technology, Department of Electrical Engineering, Computer Engineering and Networks Laboratory, Zürich, Switzerland
,
Kalyanmoy Deb
Indian Institute of Technology, Department of Mechanical Engineering, Kanpur Genetic Algorithms Laboratory, Kanpur, India

Sponsors

Fundação Luso-Americana para o Desenvolvimento
Fundação Calouste Gulbenkian
Fundação para a Ciência e a Tecnologia
Fundação Oriente
Universidade do Algarve

Publisher

Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 08 April 2003

Qualifiers

Article

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

11
Total Citations
View Citations
0
Total Downloads

Downloads (Last 12 months)0
Downloads (Last 6 weeks)0

Reflects downloads up to 17 Dec 2024

Other Metrics

View Author Metrics

Citations

Cited By

Guo XWang YWang X(2016)An objective reduction algorithm using representative Pareto solution search for many-objective optimization problemsSoft Computing - A Fusion of Foundations, Methodologies and Applications10.1007/s00500-015-1776-420:12(4881-4895)Online publication date: 1-Dec-2016
https://dl.acm.org/doi/10.1007/s00500-015-1776-4
Pinheiro RLanda-Silva DAtkin JEsparcia-Alcázar A(2015)Analysis of Objectives Relationships in Multiobjective Problems Using Trade-Off Region MapsProceedings of the 2015 Annual Conference on Genetic and Evolutionary Computation10.1145/2739480.2754721(735-742)Online publication date: 11-Jul-2015
https://dl.acm.org/doi/10.1145/2739480.2754721
Bandyopadhyay SMukherjee A(2015)An Algorithm for Many-Objective Optimization With Reduced Objective Computations: A Study in Differential EvolutionIEEE Transactions on Evolutionary Computation10.1109/TEVC.2014.233287819:3(400-413)Online publication date: 1-Jun-2015
https://dl.acm.org/doi/10.1109/TEVC.2014.2332878
Freitas ASilva RGuimarães FArnold D(2014)On the visualization of trade-offs and reducibility in many-objective optimizationProceedings of the Companion Publication of the 2014 Annual Conference on Genetic and Evolutionary Computation10.1145/2598394.2610550(1091-1098)Online publication date: 12-Jul-2014
https://dl.acm.org/doi/10.1145/2598394.2610550
Pasia JAguirre HTanaka K(2011)Improved random one-bit climbers with adaptive ε-ranking and tabu moves for many-objective optimizationProceedings of the 6th international conference on Evolutionary multi-criterion optimization10.5555/1987637.1987651(182-196)Online publication date: 5-Apr-2011
https://dl.acm.org/doi/10.5555/1987637.1987651
Purshouse RJalbă CFleming P(2011)Preference-driven co-evolutionary algorithms show promise for many-objective optimisationProceedings of the 6th international conference on Evolutionary multi-criterion optimization10.5555/1987637.1987648(136-150)Online publication date: 5-Apr-2011
https://dl.acm.org/doi/10.5555/1987637.1987648
Li MZheng JLi KYuan QShen R(2010)Enhancing diversity for average ranking method in evolutionary many-objective optimizationProceedings of the 11th international conference on Parallel problem solving from nature: Part I10.5555/1885031.1885101(647-656)Online publication date: 11-Sep-2010
https://dl.acm.org/doi/10.5555/1885031.1885101
Herrero JBerlanga ALópez J(2009)Effective evolutionary algorithms for many-specifications attainmentIEEE Transactions on Evolutionary Computation10.1109/TEVC.2008.92067713:1(151-168)Online publication date: 1-Feb-2009
https://dl.acm.org/doi/10.1109/TEVC.2008.920677
Sato HAguirre HTanaka K(2007)Controlling dominance area of solutions and its impact on the performance of MOEAsProceedings of the 4th international conference on Evolutionary multi-criterion optimization10.5555/1762545.1762552(5-20)Online publication date: 5-Mar-2007
https://dl.acm.org/doi/10.5555/1762545.1762552
Manos SPoladian LBentley PLarge M(2005)Photonic device design using multiobjective evolutionary algorithmsProceedings of the Third international conference on Evolutionary Multi-Criterion Optimization10.1007/978-3-540-31880-4_44(636-650)Online publication date: 9-Mar-2005
https://dl.acm.org/doi/10.1007/978-3-540-31880-4_44
Show More Cited By

View Options

View options

Media

Figures

Other

Tables

View Table of Contents