A Physarum-Inspired Competition Algorithm for Solving Discrete Multi-Objective Optimization Problems

Abubakr Awad, Muhammad Usman, David Lusseau, George M. Coghill, Wei Pang

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

Abstract

Many real-world problems can be naturally formulated as discrete multi-objective optimization (DMOO) problems. In this research we propose a novel bio-inspired Physarum competition algorithm (PCA) to tackle DMOO problems by modelling the Physarum discrete motility over a hexagonal cellular automaton. Our algorithm is based on the chemo-attraction forces towards food resources
(Objective Functions) and the repulsion negative forces between the competing Physarum. Numerical experimental work clearly demonstrated that our PCA algorithm had the best performance for the spread indicator against three state-of-the-art evolutionary algorithms, and its effectiveness in terms of commonly used metrics. These results have indicated the superiority of PCA in exploring
the search space and keeping diversity, this makes PCA a promising algorithm for solving DMOO problems.
Original languageEnglish
Title of host publicationGECCO 2019 Companion - Proceedings of the 2019 Genetic and Evolutionary Computation Conference Companion
EditorsManuel López-Ibáñez, Anne Auger, Thomas Stützle
Place of PublicationNew York, USA
PublisherACM
Pages195-196
Number of pages2
ISBN (Electronic)9781450367486
ISBN (Print)9781450367486
DOIs
Publication statusPublished - 13 Jul 2019
EventThe Genetic and Evolutionary Computation Conference GECCO 2019 - Prague, Czech Republic
Duration: 13 Jul 201917 Jul 2019

Conference

ConferenceThe Genetic and Evolutionary Computation Conference GECCO 2019
CountryCzech Republic
CityPrague
Period13/07/1917/07/19

Keywords

  • Physarum
  • Competition
  • DMOO
  • 2D Hexagonal grid
  • Diffusion

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