research-article

An archived-based stochastic ranking evolutionary algorithm (asrea) for multi-objective optimization

Authors:

Pierre ColletAuthors Info & Claims

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

Pages 479 - 486

https://doi.org/10.1145/1830483.1830572

Published: 07 July 2010 Publication History

Abstract

In this paper, we propose a new multi-objective optimization algorithm called Archived-based Stochastic Ranking Evolutionary Algorithm (ASREA) that ranks the population by comparing individuals with members of an archive. The stochastic comparison breaks the usual O(mn²) complexity into O(man) (m being the number of objectives, a the size of the archive and n the population size), whereas updating the archive with distinct and well-spread non-dominated solutions and developed selection strategy retain the quality of state of the art deterministic multi-objective evolutionary algorithms (MOEAs).

Comparison on ZDT and 3-objective DTLZ functions shows that ASREA converges on the Pareto-optimal front at least as well as NSGA-II and SPEA2 while reaching it much faster, and being cheaper on ranking comparisons.

References

[1]

D. W. Corne, N. R. Jerram, J. D. Knowles, M. J. Oates, and M. J. Pesa-ii: Region-based selection in evolutionary multiobjective optimization. In Proceedings of the Genetic and Evolutionary Computation Conference GECCO 2001, pages 283--290. Morgan Kaufmann Publishers, 2001.

[2]

D. W. Corne, J. D. Knowles, and M. J. Oates. The pareto envelope-based selection algorithm for multiobjective optimization. In Proceedings of the Parallel Problem Solving from Nature VI Conference, pages 839--848. Springer, 2000.

Digital Library

[3]

K. Deb and R. B. Agrawal. Simulated binary crossover for continuous search space. Complex Systems, 9(2):115--148, 1995.

[4]

K. Deb, S. Agrawal, A. Pratap, and T. Meyarivan. A fast and elitist multi-objective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation, 6(2):182--197, 2002.

Digital Library

[5]

K. Deb, L. Thiele, M. Laumanns, and E. Zitzler. Scalable test problems for evolutionary multiobjective optimization. In A. Abraham, L. Jain, and R. Goldberg, editors, Evolutionary multiobjective optimization: theoritical advances and applications, pages 105--145. Springer-Verlag, London, 2005.

[6]

L. J. Fogel, A. J. Owens, and M. J. Walsh. Artificial Intelligence through Simulated Evolution. New York: John Wiley, 1966.

[7]

C. M. Fonseca and P. J. Fleming. On the performance assessment and comparison of stochastic multiobjective optimizers. In Proceedings of Parallel Problem Solving from Nature IV (PPSN-IV), pages 584--593, 1996.

Digital Library

[8]

C. M. Fonseca, V. G. Fonseca, and L. Paquete. Exploring the performance of stochastic multiobjective optimizers with the second-order attainment functions. In Proceedings of the Third Evolutionary Multi-Criterion Optimization (EMO-05) Conference, pages 250--264, 2005.

Digital Library

[9]

V. G. Fonseca, C. M. Fonseca, and A. O. Hall. Inferential performance assessment of stochastic optimizers and the attainment function. In Proceedings of the First Evolutionary Multi-Criterion Optimization (EMO-01) Conference, pages 213--225, 2001.

Digital Library

[10]

M. P. Fourman. Compaction of symbolic layout using genetic algorithms. In Proceedings of the 1st International Conference on Genetic Algorithms, pages 141--153, Hillsdale, NJ, USA, 1985. L. Erlbaum Associates Inc.

Digital Library

[11]

D. E. Goldberg. Genetic algorithms in search, optimization and machine learning. Addison Wesley, 1989.

Digital Library

[12]

M. P. Hansen and A. Jaszkiewicz. Evaluating the Quality of Approximations to the Non-Dominated Set. Imm-rep-1998-7, Technical University of Denmark, 1998.

[13]

J. Horn, N. Nafploitis, and D. E. Goldberg. A niched Pareto genetic algorithm for multi-objective optimization. In Proceedings of the First IEEE Conference on Evolutionary Computation, pages 82--87, 1994.

[14]

KanGAL. Nsga-ii in c with gnuplot (real + binary + constraint handling): Revision 1.1. http://www.iitk.ac.in/kangal/codes.shtml, June 2005.

[15]

J. Knowles and D. Corne. Properties of an adaptive archiving algorithm for storing nondominated vectors. IEEE Transactions on Evolutionary Computation, 7(2):100--116, 2003.

Digital Library

[16]

J. Knowles, L. Thiele, and E. Zitzler. A Tutorial on the Performance Assessment of Stochastic Multiobjective Optimizers. TIK Report 214, Computer Engineering and Networks Laboratory (TIK), ETH Zurich, Feb. 2006.

[17]

M. Laumanns, L. Thiele, K. Deb, and E. Zitzler. Archiving with Guaranteed Convergence And Diversity in Multi-objective Optimization. In Genetic and Evolutionary Computation Conference (GECCO 2002), pages 439--447, New York, NY, USA, July 2002. Morgan Kaufmann Publishers.

Digital Library

[18]

G. T. Parks and I. Miller. Selective breeding in a multiobjective genetic algorithm. In A. E. Eiben, M. Schoenauer, and H. P. Schwefel, editors, Proceedings of the Parallel Problem Solving from Nature V (PPSN-V), pages 250--259, Amsterdam, Holland, 1998. Springer-Verlag.

Digital Library

[19]

TIK. A platform and programming language independent interface for search algorithms. http://www.tik.ethz.ch/~sop/pisa/.

[20]

E. Zitzler, K. Deb, and L. Thiele. Comparison of multiobjective evolutionary algorithms : Empirical results. Evolutionary Computation Journal, 8(2):125--148, 2000.

Digital Library

[21]

E. Zitzler, M. Laumanns, and L. Thiele. SPEA2: Improving the Strength Pareto Evolutionary Algorithm for Multiobjective Optimization. In K. Giannakoglou et al., editors, Evolutionary Methods for Design, Optimisation and Control with Application to Industrial Problems (EUROGEN 2001), pages 95--100. International Center for Numerical Methods in Engineering (CIMNE), 2002.

[22]

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

Digital Library

[23]

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

Digital Library

Cited By

Patil M(2020)Improved performance in multi-objective optimization using external archiveSādhanā10.1007/s12046-020-1309-445:1Online publication date: 16-Mar-2020
https://doi.org/10.1007/s12046-020-1309-4
Leonteva AParrend PJeannin-Girardon ACollet P(2020)Fast Evolutionary Algorithm for Solving Large-Scale Multi-objective ProblemsArtificial Evolution10.1007/978-3-030-45715-0_7(82-95)Online publication date: 29-Apr-2020
https://doi.org/10.1007/978-3-030-45715-0_7
Saikia RSharma D(2019)Hybridizing Evolutionary Multi-objective Algorithm Using Random Mutations and Local SearchesAdvances in Computational Methods in Manufacturing10.1007/978-981-32-9072-3_75(899-908)Online publication date: 18-Oct-2019
https://doi.org/10.1007/978-981-32-9072-3_75
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Index Terms

An archived-based stochastic ranking evolutionary algorithm (asrea) for multi-objective optimization
1. Theory of computation

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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]

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SIGEVO: ACM Special Interest Group on Genetic and Evolutionary Computation

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Association for Computing Machinery

New York, NY, United States

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Published: 07 July 2010

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GECCO '10

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SIGEVO

GECCO '10: Genetic and Evolutionary Computation Conference

July 7 - 11, 2010

Oregon, Portland, USA

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

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Cited By

Patil M(2020)Improved performance in multi-objective optimization using external archiveSādhanā10.1007/s12046-020-1309-445:1Online publication date: 16-Mar-2020
https://doi.org/10.1007/s12046-020-1309-4
Leonteva AParrend PJeannin-Girardon ACollet P(2020)Fast Evolutionary Algorithm for Solving Large-Scale Multi-objective ProblemsArtificial Evolution10.1007/978-3-030-45715-0_7(82-95)Online publication date: 29-Apr-2020
https://doi.org/10.1007/978-3-030-45715-0_7
Saikia RSharma D(2019)Hybridizing Evolutionary Multi-objective Algorithm Using Random Mutations and Local SearchesAdvances in Computational Methods in Manufacturing10.1007/978-981-32-9072-3_75(899-908)Online publication date: 18-Oct-2019
https://doi.org/10.1007/978-981-32-9072-3_75
Maitre OKrüger FQuerry SLachiche NCollet P(2018)EASEA: specification and execution of evolutionary algorithms on GPGPUSoft Computing - A Fusion of Foundations, Methodologies and Applications10.1007/s00500-011-0718-z16:2(261-279)Online publication date: 29-Dec-2018
https://dl.acm.org/doi/10.1007/s00500-011-0718-z
Hamze NCollet PEssert C(2017)Evolutionary approaches for surgical path planning: A quantitative study on Deep Brain Stimulation2017 IEEE Congress on Evolutionary Computation (CEC)10.1109/CEC.2017.7969428(1087-1094)Online publication date: Jun-2017
https://doi.org/10.1109/CEC.2017.7969428
Maitre OKruger FSharma DQuerry SLachiche NCollet P(2017)Parallelizing Evolutionary Algorithms on GPGPU Cards with the EASEA PlatformProgramming multi‐core and many‐core computing systems10.1002/9781119332015.ch15(301-319)Online publication date: 27-Jan-2017
https://doi.org/10.1002/9781119332015.ch15
Fodorean DIdoumghar LSzabo L(2013)Motorization for an Electric Scooter by Using Permanent-Magnet Machines Optimized Based on a Hybrid Metaheuristic AlgorithmIEEE Transactions on Vehicular Technology10.1109/TVT.2012.221597062:1(39-49)Online publication date: Jan-2013
https://doi.org/10.1109/TVT.2012.2215970
Lepagnot JIdoumghar LFodorean D(2013)Hybrid Imperialist Competitive Algorithm with Simplex ApproachProceedings of the 2013 IEEE International Conference on Systems, Man, and Cybernetics10.1109/SMC.2013.419(2454-2459)Online publication date: 13-Oct-2013
https://dl.acm.org/doi/10.1109/SMC.2013.419
Sharma DCollet P(2013)Implementation Techniques for Massively Parallel Multi-objective OptimizationMassively Parallel Evolutionary Computation on GPGPUs10.1007/978-3-642-37959-8_13(267-286)Online publication date: 6-Jul-2013
https://doi.org/10.1007/978-3-642-37959-8_13
Sharma DCollet P(2010)GPGPU-compatible archive based stochastic ranking evolutionary algorithm (G-ASREA) for multi-objective optimizationProceedings of the 11th international conference on Parallel problem solving from nature: Part II10.5555/1887255.1887268(111-120)Online publication date: 11-Sep-2010
https://dl.acm.org/doi/10.5555/1887255.1887268
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