Using Parallel Strategies to Speed up Pareto Local Search

  • Jialong Shi
  • Qingfu Zhang
  • Bilel Derbel
  • Arnaud Liefooghe
  • Sébastien Verel
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10593)

Abstract

Pareto Local Search (PLS) is a basic building block in many state-of-the-art multiobjective combinatorial optimization algorithms. However, the basic PLS requires a long time to find high-quality solutions. In this paper, we propose and investigate several parallel strategies to speed up PLS. These strategies are based on a parallel multi-search framework. In our experiments, we investigate the performances of different parallel variants of PLS on the multiobjective unconstrained binary quadratic programming problem. Each PLS variant is a combination of the proposed parallel strategies. The experimental results show that the proposed approaches can significantly speed up PLS while maintaining about the same solution quality. In addition, we introduce a new way to visualize the search process of PLS on two-objective problems, which is helpful to understand the behaviors of PLS algorithms.

Keywords

Multiobjective combinatorial optimization Pareto local search Parallel metaheuristics Unconstrained binary quadratic programming 

Notes

Acknowledgments

The work described in this paper was supported by the National Science Foundation of China under Grant 61473241, and a grant from ANR/RCC Joint Research Scheme sponsored by the Research Grants Council of the Hong Kong Special Administrative Region, China (Project No. A-CityU101/16), and France National Research Agency (ANR-16-CE23-0013-01).

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Jialong Shi
    • 1
    • 2
  • Qingfu Zhang
    • 1
    • 2
  • Bilel Derbel
    • 3
    • 4
  • Arnaud Liefooghe
    • 3
    • 4
  • Sébastien Verel
    • 5
  1. 1.Department of Computer ScienceCity University of Hong KongHong KongHong Kong
  2. 2.The City University of Hong Kong Shenzhen Research InstituteShenzhenChina
  3. 3.CNRS, Centrale Lille, UMR 9189 – CRIStALUniversity of LilleLilleFrance
  4. 4.Dolphin, Inria Lille – Nord EuropeLilleFrance
  5. 5.LISICUniversity Littoral Côte d’OpaleCalaisFrance

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