research-article

A grid-based fitness strategy for evolutionary many-objective optimization

Authors:

Qizhao YuanAuthors Info & Claims

GECCO '10: Proceedings of the 12th annual conference on Genetic and evolutionary computation

Pages 463 - 470

https://doi.org/10.1145/1830483.1830570

Published: 07 July 2010 Publication History

Abstract

Grid has been widely used in the field of evolutionary multi-objective optimization (EMO) due to its property combining convergence and diversity naturally. Most EMO algorithms of grid-based fitness perform well on problems with two or three objectives, but encounter difficulties in their scalability to many-objective optimization. This paper develops the potential of using grid technique to balance convergence and diversity in fitness for many-objective optimization problems. To strengthen selection pressure and refine comparison level, three hierarchical grid-based criterions are incorporated into fitness to establish a completer order among individuals. Moreover, an adaptive fitness penalty mechanism in environmental selection is employed to guarantee the diversity of archive memory. Based on an extensive comparative study with three other EMO algorithms, the proposed algorithm is found to be remarkably successful in finding well-converged and well-distributed solution set.

References

[1]

Salem F. Adra and Peter F. Fleming. A Diversity Management Operator for Evolutionary Many-Objective Optimisation. Evolutionary Multi-Criterion Optimization. 5th International Conference, EMO 2009, pp. 81--94, Springer, Nantes, France, 2009.

Digital Library

[2]

P. J. Bentley and J. P. Wakefield. Finding Acceptable Solutions in the Pareto-Optimal Range using Multiobjective Genetic Algorithms, Soft Computing in Engineering Design and Manufacturing, Part 5, pages 231--240, London, June 1997.

[3]

C. A. Coello Coello, D. A. Van Veldhuizen, and G. B. Lamont, Evolutionary Algorithms for Solving Multi-Objective Problems. Second Edition, Springer, New York, September 2007.

Digital Library

[4]

D. Corne and J. Knowles. Techniques for highly multiobjective optimization: Some non-dominated points are better than others. Proc. of 2007 Genetic and Evolutionary Computation Conference (GECCO'2007), pp. 773--780, London, July 7-11, 2007.

Digital Library

[5]

D. Corne, J. Knowles, and M. Oates, "The Pareto Envelope-based Selection Algorithm for Multiobjective Optimization", In Parallel Problem Solving from Nature (PPSN VI), pp. 839--848, Springer, 2000.

Digital Library

[6]

K. Deb, Multi-Objective Optimization using Evolutionary Algorithms. New York: Wiley, 2001.

Digital Library

[7]

K. Deb, M. Mohan, and S. Mishra. Evaluating the epsilon-Domination Based Multi-Objective Evolutionary Algorithm for a Quick Computation of Pareto-Optimal Solutions. Evolutionary Computation. 13(4): 501--525, 2005.

Digital Library

[8]

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

Digital Library

[9]

K. Deb, L. Thiele, M. Laumanns, and E. Zitzler. Scalable test problems for evolutionary multi-objective optimization. Evolutionary Multiobjective Optimization. Theoretical Advances and Applications, Berlin, Germany: Springer-Verlag, pp. 105--145, 2005.

[10]

N. Drechsler, R. Drechsler, and B. Becker. Multi-objective optimization based on relation favour. Evolutionary Multi-Criterion Optimization - EMO 2001, pp. 154--166, Springer, Berlin, March 2001.

Digital Library

[11]

M. Farina and P. Amato. On the Optimal Solution Definition for Many-criteria Optimization Problems, in Proceedings of the NAFIPS-FLINT International Conference'2002, pp. 233--238, IEEE Service Center, Piscataway, New Jersey, June 2002.

[12]

M. Farina and P. Amato. A fuzzy definition of "optimality" for many-criteria optimization problems. IEEE Transactions on Systems, Man, and Cybernetics Part A Systems and Humans 34(3): 315--326, 2004.

[13]

Evan J. Hughes. Fitness Assignment Methods for Many-Objective Problems, Multi-Objective Problem Solving from Nature: From Concepts to Applications, pp. 307--329, Springer, Berlin, 2008.

[14]

Hisao Ishibuchi, Noritaka Tsukamoto and Yusuke Nojima. Evolutionary many-objective optimization: A short review, in 2008 Congress on Evolutionary Computation (CEC'2008), pp. 2424--2431, IEEE Service Center, Hong Kong, June 2008.

[15]

Antonio López Jaimes and Carlos A. Coello Coello. Study of Preference Relations in Many-Objective Optimization, 2009 Genetic and Evolutionary Computation Conference (GECCO'2009), pp. 611--618, ACM Press, Montreal, Canada, 2009.

Digital Library

[16]

V. Khara, X. Yao, and K. Deb. Performance scaling of multi-objective evolutionary algorithms. Evolutionary Multi-Criterion Optimization - EMO 2003, pp. 367--390, Springer, Berlin, April 2003.

Digital Library

[17]

J. Knowles and D. Corne. Properties of an Adaptive Archiving Algorithm for Storing Nondominated Vectors, IEEE Transactions on Evolutionary Computation, 7(2): 100--116, April 2003.

Digital Library

[18]

J. Knowles and D. Corne. Quantifying the Effects of Objective Space Dimension in Evolutionary Multiobjective Optimization. Evolutionary Multi-Criterion Optimization, 4th International Conference, (EMO 2007), Springer. Matshushima, Japan, pp. 757--771.

Digital Library

[19]

S. Mostaghim and H. Schmeck. Distance based ranking in many-objective particle swarm optimization. In Parallel Problem Solving from Nature - PPSN 2008, Springer, pp. 753--762.

[20]

Francesco di Pierro, Shoon-Thiam Khu and Dragan A. Savic. An Investigation on Preference Order Ranking Scheme for Multiobjective Evolutionary Optimization, IEEE Transactions on Evolutionary Computation, 11(1): 17--45, February 2007.

Digital Library

[21]

Robin C. Purshouse and Peter J. Fleming. On the Evolutionary Optimization of Many Conflicting Objectives, IEEE Transactions on Evolutionary Algorithms, 11(6): 770--784, December 2007.

Digital Library

[22]

H. Sato, H. E. Aguirre, and K. Tanaka. Controlling dominance area of solutions and its impact on the performance of MOEAs. Evolutionary Multi-Criterion Optimization - EMO 2007, pp. 5--20, Springer, Berlin, March 2007.

Digital Library

[23]

T. Wagner, N. Beume, and B. Naujoks. Pareto-, aggregation-, and indicator-based methods in many-objective optimization. Evolutionary Multi-Criterion Optimization - EMO 2007, pp. 742--756, Springer, Berlin, March 2007.

Digital Library

[24]

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

Digital Library

[25]

E. Zitzler, M. Laumanns, and S. Bleuler, "A Tutorial on Evolutionary Multiobjective Optimization," Metaheuristics for Multiobjective Optimisation. Springer, Berlin, pp. 3--37, 2004.

Digital Library

[26]

E. Zitzler, M. Laumanns, and L. Thiele. SPEA2: Improving the strength Pareto evolutionary algorithm for multiobjective optimization. In Evolutionary Methods for Design, Optimisation and Control. Barcelona, Spain: CIMNE, pp. 95--100, 2002

Cited By

Wang LWang LJiang QWang ZZhu WWang Z(2025)An adaptive transfer strategy guided by reference vectors for many-objective optimization problemsThe Journal of Supercomputing10.1007/s11227-024-06547-281:1Online publication date: 1-Jan-2025
https://dl.acm.org/doi/10.1007/s11227-024-06547-2
Kalita KJangir PKumar APandya SAbualigah L(2024)A novel many-objective symbiotic organism search algorithm for industrial engineering problemsInternational Journal on Interactive Design and Manufacturing (IJIDeM)10.1007/s12008-024-02143-zOnline publication date: 1-Nov-2024
https://doi.org/10.1007/s12008-024-02143-z
Panagant NKumar STejani GPholdee NBureerat S(2023)Many‑objective Meta-heuristic methods for Solving Constrained Truss Optimisation Problems: A Comparative analysis.MethodsX10.1016/j.mex.2023.102181(102181)Online publication date: Apr-2023
https://doi.org/10.1016/j.mex.2023.102181
Show More Cited By

Index Terms

A grid-based fitness strategy for evolutionary many-objective optimization
1. Computing methodologies
  1. Artificial intelligence
    1. Control methods
    2. Search methodologies
2. Theory of computation
  1. Design and analysis of algorithms
    1. Algorithm design techniques
      1. Dynamic programming

Recommendations

A reference points-based evolutionary algorithm for many-objective optimization
GECCO Comp '14: Proceedings of the Companion Publication of the 2014 Annual Conference on Genetic and Evolutionary Computation

Many-objective optimization problems are common in real-world applications, few evolutionary optimization methods, however, are suitable for them up to date due to their difficulties. We proposed a reference points-based evolutionary algorithm (RPEA) to ...
An improved NSGA-III procedure for evolutionary many-objective optimization
GECCO '14: Proceedings of the 2014 Annual Conference on Genetic and Evolutionary Computation

Many-objective (four or more objectives) optimization problems pose a great challenge to the classical Pareto-dominance based multi-objective evolutionary algorithms (MOEAs), such as NSGA-II and SPEA2. This is mainly due to the fact that the selection ...
Many-objective evolutionary optimization based on reference points

Graphical abstractThis figure illustrates the flowchart of the proposed reference points-based evolutionary algorithm (RPEA). Its basic procedure is similar to most generational multi-objective evolutionary algorithms. First, an initial population is ...

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences

GECCO '10: Proceedings of the 12th annual conference on Genetic and evolutionary computation

July 2010

1520 pages

ISBN:9781450300728

DOI:10.1145/1830483

General Chair:
Martin Pelikan
University of Missouri, USA
,
Program Chair:
Jürgen Branke
University of Warwick, Coventry, UK

Copyright © 2010 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Sponsors

SIGEVO: ACM Special Interest Group on Genetic and Evolutionary Computation

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 07 July 2010

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Conference

GECCO '10

Sponsor:

SIGEVO

GECCO '10: Genetic and Evolutionary Computation Conference

July 7 - 11, 2010

Oregon, Portland, USA

Acceptance Rates

Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

27
Total Citations
View Citations
265
Total Downloads

Downloads (Last 12 months)3
Downloads (Last 6 weeks)0

Reflects downloads up to 27 Feb 2025

Other Metrics

View Author Metrics

Citations

Cited By

Wang LWang LJiang QWang ZZhu WWang Z(2025)An adaptive transfer strategy guided by reference vectors for many-objective optimization problemsThe Journal of Supercomputing10.1007/s11227-024-06547-281:1Online publication date: 1-Jan-2025
https://dl.acm.org/doi/10.1007/s11227-024-06547-2
Kalita KJangir PKumar APandya SAbualigah L(2024)A novel many-objective symbiotic organism search algorithm for industrial engineering problemsInternational Journal on Interactive Design and Manufacturing (IJIDeM)10.1007/s12008-024-02143-zOnline publication date: 1-Nov-2024
https://doi.org/10.1007/s12008-024-02143-z
Panagant NKumar STejani GPholdee NBureerat S(2023)Many‑objective Meta-heuristic methods for Solving Constrained Truss Optimisation Problems: A Comparative analysis.MethodsX10.1016/j.mex.2023.102181(102181)Online publication date: Apr-2023
https://doi.org/10.1016/j.mex.2023.102181
Wang ZLi QLi GZhang Q(2023)Multi-objective decomposition evolutionary algorithm with objective modification-based dominance and external archiveApplied Soft Computing10.1016/j.asoc.2023.111006149(111006)Online publication date: Dec-2023
https://doi.org/10.1016/j.asoc.2023.111006
Prajapati A(2022)Software module clustering using grid-based large-scale many-objective particle swarm optimizationSoft Computing10.1007/s00500-022-07182-w26:17(8709-8730)Online publication date: 29-May-2022
https://doi.org/10.1007/s00500-022-07182-w
Li LWu HHu XSheng G(2021)Evolutionary Algorithm for Multiobjective Optimization Based on Density Estimation RankingWireless Communications and Mobile Computing10.1155/2021/42966422021(1-18)Online publication date: 5-Jul-2021
https://doi.org/10.1155/2021/4296642
Prajapati A(2021)A comparative study of many-objective optimizers on large-scale many-objective software clustering problemsComplex & Intelligent Systems10.1007/s40747-021-00270-87:2(1061-1077)Online publication date: 22-Jan-2021
https://doi.org/10.1007/s40747-021-00270-8
Biswas APal T(2020)A comparison between metaheuristics for solving a capacitated fixed charge transportation problem with multiple objectivesExpert Systems with Applications10.1016/j.eswa.2020.114491(114491)Online publication date: Dec-2020
https://doi.org/10.1016/j.eswa.2020.114491
Liu JWang YWei SWu XTong W(2019)A Parameterless Penalty Rule-Based Fitness Estimation for Decomposition-Based Many-Objective Optimization Evolutionary AlgorithmIEEE Access10.1109/ACCESS.2019.29206987(81701-81716)Online publication date: 2019
https://doi.org/10.1109/ACCESS.2019.2920698
Xue YLi MShepperd MLauria SLiu X(2019)A novel aggregation-based dominance for Pareto-based evolutionary algorithms to configure software product linesNeurocomputing10.1016/j.neucom.2019.06.075364:C(32-48)Online publication date: 28-Oct-2019
https://dl.acm.org/doi/10.1016/j.neucom.2019.06.075
Show More Cited By

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Figures

Tables

Media

View Table of Conten