Improved archiving and search strategies for multi agent collaborative search

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Abstract

This paper presents a new archiving strategy and some modified search heuristics for the Multi Agent Collaborative Search algorithm (MACS). MACS is a memetic scheme for multi-objective optimisation that combines the local exploration of the neighbourhood of some virtual agents with social actions to advance towards the Pareto front. The new archiving strategy is based on the physical concept of minimising the potential energy of a cloud of points each of which repels the others. Social actions have been modified to better exploit the information in the archive and local actions dynamically adapt the maximum number of coordinates explored in the pattern search heuristic. The impact of these modifications is tested on a standard benchmark and the results are compared against MOEA/D and a previous version of MACS. Finally, a real space related problem is tackled.

Original languageEnglish
Title of host publicationAdvances in Evolutionary and Deterministic Methods for Design, Optimization and Control in Engineering and Sciences
Subtitle of host publicationComputational Methods in Applied Sciences
Place of PublicationCham, Switzerland
PublisherSpringer
Pages435-455
Number of pages21
ISBN (Print)9783319899862
DOIs
Publication statusPublished - 1 Aug 2019

Publication series

NameComputational Methods in Applied Sciences
Volume48
ISSN (Print)1871-3033

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Keywords

  • Multi Agent Collaborative Search algorithm (MACS)
  • Pareto front
  • astronautics
  • social actions

Cite this

Ricciardi, L. A., & Vasile, M. (2019). Improved archiving and search strategies for multi agent collaborative search. In Advances in Evolutionary and Deterministic Methods for Design, Optimization and Control in Engineering and Sciences: Computational Methods in Applied Sciences (pp. 435-455). (Computational Methods in Applied Sciences; Vol. 48). Cham, Switzerland: Springer. https://doi.org/10.1007/978-3-319-89988-6_26