A Parallel Evolutionary System for Multi-objective Optimisation

Mohammad Hamdan, Gunter Rudolph, Nicola Hochstrate

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Parallel evolutionary algorithms have been used for solving multiobjective optimization problems. The aim is to find or approximate the Pareto optimal set in a reasonable time. In this work, we present a new approach that divides the objective search-space into different partitions and assigns each processor its corresponding partition. Each processor will try to find the set of solutions for its partition only. The sub-Pareto fronts will be combined later and the parallelisation approach is based on a mutli-start approach by having independent algorithm on every processor with its own starting points. Experimental results on well known test cases showed that the proposed method outperformed several state-of-the-art evolutionary algorithms regarding convergence to the true Pareto front and gave very competitive results when considering the hypervolume metric. Also, superlinear speedup results were achieved for all test functions.

Original languageEnglish
Title of host publication2020 IEEE Congress on Evolutionary Computation (CEC)
PublisherIEEE
ISBN (Electronic)9781728169293
DOIs
Publication statusPublished - 3 Sep 2020
Event2020 IEEE Congress on Evolutionary Computation - Virtual, Glasgow, United Kingdom
Duration: 19 Jul 202024 Jul 2020

Conference

Conference2020 IEEE Congress on Evolutionary Computation
Abbreviated titleCEC 2020
CountryUnited Kingdom
CityVirtual, Glasgow
Period19/07/2024/07/20

Keywords

  • Evolutionary Multiobjective Algorithms
  • Multiobjective Optimisation
  • Objective Search Space
  • Parallelisation

ASJC Scopus subject areas

  • Control and Optimization
  • Decision Sciences (miscellaneous)
  • Artificial Intelligence
  • Computer Vision and Pattern Recognition
  • Hardware and Architecture

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