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 (https://doi.org/10.1007/978-3-319-44003-3_10)

Full text not available in this repository.Request a copy

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 ORCID logoORCID: https://orcid.org/0000-0001-8302-6465 and Ricciardi, Lorenzo ORCID logoORCID: https://orcid.org/0000-0002-0895-7961; Schütze, Oliver, Trujillo, Leonardo, Legrand, Pierrick and Maldonado, Yazmin
CORE (COnnecting REpositories)