Using Parallel Strategies to Speed up Pareto Local Search

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journal

3 Scopus Citations
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Author(s)

Detail(s)

Original languageEnglish
Pages (from-to)62-74
Journal / PublicationLecture Notes in Computer Science
Volume10593
Publication statusPublished - Nov 2017

Conference

Title11th International Conference on Simulated Evolution and Learning ( SEAL 2017)
LocationSouthern University of Science and Technology
PlaceChina
CityShenzhen
Period10 - 13 November 2017

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.

Research Area(s)

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

Citation Format(s)

Using Parallel Strategies to Speed up Pareto Local Search. / Shi, Jialong; Zhang, Qingfu; Derbel, Bilel; Liefooghe, Arnaud; Verel, Sébastien.

In: Lecture Notes in Computer Science, Vol. 10593, 11.2017, p. 62-74.

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journal