Multi agent collaborative search

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1 Citation (Scopus)

Abstract

This chapter presents an overview of Multi Agent Collaborative Search (MACS), for multi-objective optimisation with an analysis of different heuristics for local search. In particular the effects of simple inertia and differential evolution operators in combination with pattern search and gradient methods are investigated. Different benchmarks are used to demonstrate the effectiveness of theMACS framework and of the heuristics for both local and global search. The MACS framework is tested on two sets of academic problems and two real space mission design problems using the IGD and the success rate as performance metrics. The performance of MACS is compared against three known multi-objective optimisation algorithms: NSGA-II, MOAED and MTS.

Original languageEnglish
Title of host publicationNEO 2015
Subtitle of host publicationResults of the Numerical and Evolutionary Optimization Workshop NEO 2015 held at September 23-25 2015 in Tijuana, Mexico
EditorsOliver Schütze , Leonardo Trujillo, Pierrick Legrand, Yazmin Maldonado
Place of PublicationSwitzerland
PublisherSpringer International Publishing AG
Pages223-252
Number of pages30
Volume663
ISBN (Print)9783319440026
DOIs
Publication statusPublished - 11 Sep 2016

Publication series

NameStudies in Computational Intelligence
PublisherSpringer
Volume663
ISSN (Print)1860-949X

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Keywords

  • evolutionary algorithms
  • local search
  • multi-objective optimisation
  • multiagent collaborative search

Cite this

Vasile, M., & Ricciardi, L. (2016). Multi agent collaborative search. In O. Schütze , L. Trujillo, P. Legrand, & Y. Maldonado (Eds.), NEO 2015: Results of the Numerical and Evolutionary Optimization Workshop NEO 2015 held at September 23-25 2015 in Tijuana, Mexico (Vol. 663, pp. 223-252). (Studies in Computational Intelligence; Vol. 663). Switzerland: Springer International Publishing AG. https://doi.org/10.1007/978-3-319-44003-3_10