skip to main content
10.1145/2598394.2610011acmconferencesArticle/Chapter ViewAbstractPublication PagesgeccoConference Proceedingsconference-collections
technical-note

A probabilistic pareto local search based on historical success counting for multiobjective optimization

Published: 12 July 2014 Publication History

Abstract

In this paper, we propose a multiobjective probabilistic Pareto local search to address combinatorial optimization problems (COPs). The probability is determined by the success counts of local search offspring entering an external domination archive and this probabilistic information is used to further guide the selection of promising solutions for Pareto local search. In addition, simulated annealing is integrated in this framework as the local refinement process. This multiobjective probabilistic Pareto local search algorithm (MOPPLS), is tested on two famous COPs and compared with some well-known multiobjective evolutionary algorithms. Experimental results suggest that MOPPLS outperforms other compared algorithms.

References

[1]
A. Alsheddy and E. Tsang. Guided pareto local search based frameworks for biobjective optimization. In IEEE Congress on Evolutionary Computation (CEC), pages 1--8, 2010.
[2]
X. Cai, O. Wei, and Z. Huang. Evolutonary approches for multi-objective next release problem. Computing and Informatics, (4):847--875, 2012.
[3]
K. Deb. Multi-Objective Optimization Using Evolutionary Algorithms. John Wiley & Sons, Inc., New York, NY, USA, 2001.
[4]
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, April 2002.
[5]
Eclipse. http://www.eclipse.org/, July 2011.
[6]
C. García-Martínez, O. Cordón, and F. Herrera. A taxonomy and an empirical analysis of multiple objective ant colony optimization algorithms for the bi-criteria TSP. European Journal of Operational Research, 180(1):116--148, July 2007.
[7]
A. Gaspar-Cunha. A Multi-Objective Evolutionary Algorithm for Solving Traveling Salesman Problems: Application to the Design of Polymer Extruders. In B. Ribeiro, R. F. Albrecht, A. Dobnikar, D. W. Pearson, and N. C. Steele, editors, Adaptive and Natural Computing Algorithms, pages 189--193, Coimbra, Portugal, March 2005. Springer.
[8]
Gnome. http://www.gnome.org/, July 2011.
[9]
D. C. Grosan and A. Abraham. Hybrid evolutionary algorithms: Methodologies, architectures, and reviews. In Hybrid Evolutionary Algorithms. Springer, Berlin, Germany, 2007.
[10]
L. Ke, Q. Zhang, and R. Battiti. A simple yet efficient multiobjective combinatorial optimization method using decompostion and Pareto local search. IEEE Trans on Cybernetics, accepted, 2014.
[11]
Y.-C. Liang and M.-H. Lo. Multi-objective redundancy allocation optimization using a variable neighborhood search algorithm. J. Heuristics, 16(3):511--535, 2010.
[12]
T. Lust and A. Jaszkiewicz. Speed-up techniques for solving large-scale biobjective tsp. Computers & OR, 37(3):521--533, 2010.
[13]
T. Lust and J. Teghem. Two-phase pareto local search for the biobjective traveling salesman problem. Journal of Heuristics, 16(3):475--510, 2010.
[14]
Q. H. Nguyen, Y.-S. Ong, and M.-H. Lim. A probabilistic memetic framework. IEEE Trans. Evolutionary Computation, 13(3):604--623, 2009.
[15]
U. M. S. Saha and K. Deb. A simulated annealing-based multiobjective optimization algorithm: Amosa. IEEE Transactions on Evolutionary Computation, 12(3):269--283, 2008.
[16]
V. A. Shim, K. C. Tan, and C. Y. Cheong. A hybrid estimation of distribution algorithm with decomposition for solving the multiobjective multiple traveling salesman problem. IEEE Transactions on Systems, Man, and Cybernetics, Part C, 42(5):682--691, 2012.
[17]
K. Sindhya, K. Miettinen, and K. Deb. A hybrid framework for evolutionary multi-objective optimization. IEEE Trans. Evolutionary Computation, 17(4):495--511, 2013.
[18]
W. Stadler. A survey of multicriteria optimization or the vector maximum problem, part i: 1776--1960. Journal of Optimization Theory and Applications, 29(1):1--52, 1979.
[19]
D. Sudholt. Local search in evolutionary algorithms: The impact of the local search frequency. In 17th International Symposium on Algorithms and Computation(ISAAC), 2006.
[20]
J. Xuan, H. Jiang, Z. Ren, and Z. Luo. Solving the large scale next release problem with a backbone-based multilevel algorithm. IEEE Trans. Software Eng., 38(5):1195--1212, 2012.
[21]
Y. Zhang, M. Harman, and S. A. Mansouri. The Multi-Objective Next Release Problem. In D. Thierens, editor, 2007 Genetic and Evolutionary Computation Conference (GECCO'2007), volume 1, pages 1129--1136, London, UK, July 2007. ACM Press.

Index Terms

  1. A probabilistic pareto local search based on historical success counting for multiobjective optimization

    Recommendations

    Comments

    Information & Contributors

    Information

    Published In

    cover image ACM Conferences
    GECCO Comp '14: Proceedings of the Companion Publication of the 2014 Annual Conference on Genetic and Evolutionary Computation
    July 2014
    1524 pages
    ISBN:9781450328814
    DOI:10.1145/2598394
    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

    Publisher

    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 12 July 2014

    Permissions

    Request permissions for this article.

    Check for updates

    Author Tags

    1. aggregation
    2. multiobjective optimization
    3. parteo local search
    4. probabilistic

    Qualifiers

    • Technical-note

    Conference

    GECCO '14
    Sponsor:
    GECCO '14: Genetic and Evolutionary Computation Conference
    July 12 - 16, 2014
    BC, Vancouver, Canada

    Acceptance Rates

    GECCO Comp '14 Paper Acceptance Rate 180 of 544 submissions, 33%;
    Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

    Contributors

    Other Metrics

    Bibliometrics & Citations

    Bibliometrics

    Article Metrics

    • 0
      Total Citations
    • 106
      Total Downloads
    • Downloads (Last 12 months)1
    • Downloads (Last 6 weeks)0
    Reflects downloads up to 20 Jan 2025

    Other Metrics

    Citations

    View Options

    Login options

    View options

    PDF

    View or Download as a PDF file.

    PDF

    eReader

    View online with eReader.

    eReader

    Media

    Figures

    Other

    Tables

    Share

    Share

    Share this Publication link

    Share on social media