Multi agent collaborative search

Vasile, Massimiliano and Ricciardi, Lorenzo; Schütze, Oliver and Trujillo, Leonardo and Legrand, Pierrick and Maldonado, Yazmin, eds. (2016) Multi agent collaborative search. In: NEO 2015. Studies in Computational Intelligence, 663 . Springer International Publishing AG, Switzerland, pp. 223-252. ISBN 9783319440026

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Official URL: https://doi.org/10.1007/978-3-319-44003-3_10

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.

ORCID iDs

Vasile, Massimiliano

and Ricciardi, Lorenzo

; Schütze, Oliver, Trujillo, Leonardo, Legrand, Pierrick and Maldonado, Yazmin

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Item metadata

Item type:	Book Section
ID code:	58771
Dates:	Date Event 11 September 2016 Published 24 August 2016 Published Online
Keywords:	evolutionary algorithms, local search, multi-objective optimisation, multiagent collaborative search, Mechanical engineering and machinery, Artificial Intelligence
Subjects:	Technology > Mechanical engineering and machinery
Department:	Faculty of Engineering > Mechanical and Aerospace Engineering Technology and Innovation Centre > Advanced Engineering and Manufacturing
Depositing user:	Pure Administrator
Date deposited:	23 Nov 2016 14:38
Last modified:	29 Jan 2021 03:09
Related URLs:	Scopus publication
URI:	https://strathprints.strath.ac.uk/id/eprint/58771

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