@inbook{d9f72b396971480f9641468b0ab556f6,
title = "Multi agent collaborative search",
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.",
keywords = "evolutionary algorithms, local search, multi-objective optimisation, multiagent collaborative search",
author = "Massimiliano Vasile and Lorenzo Ricciardi",
year = "2016",
month = sep,
day = "11",
doi = "10.1007/978-3-319-44003-3_10",
language = "English",
isbn = "9783319440026",
volume = "663",
series = "Studies in Computational Intelligence",
publisher = "Springer International Publishing AG",
pages = "223--252",
editor = "{Sch{\"u}tze }, Oliver and Leonardo Trujillo and Pierrick Legrand and {Maldonado }, Yazmin",
booktitle = "NEO 2015",
address = "Switzerland",
}