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

1 Citation (Scopus)

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 exploringthe search space and keeping diversity, this makes PCA a promising algorithm for solving DMOO problems.
Original languageEnglish
Title of host publicationGECCO '19: Proceedings of the Genetic and Evolutionary Computation Conference Companion
Place of PublicationNew York, USA
PublisherAssociation for Computing Machinery
Pages195-196
Number of pages2
ISBN (Print)9781450367486
DOIs
Publication statusPublished - 13 Jul 2019

Keywords

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

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  • Cite this

    Awad, A., Usman, M., Lusseau, D., Coghill, G. M., & Pang, W. (2019). A Physarum-Inspired Competition Algorithm for Solving Discrete Multi-Objective Optimization Problems. In GECCO '19: Proceedings of the Genetic and Evolutionary Computation Conference Companion (pp. 195-196). Association for Computing Machinery. https://doi.org/10.1145/3319619.3322030