Improved archiving and search strategies for multi agent collaborative search

Ricciardi, Lorenzo A. and Vasile, Massimiliano; (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 . Springer, Cham, Switzerland, pp. 435-455. ISBN 9783319899862 (https://doi.org/10.1007/978-3-319-89988-6_26)

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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.

ORCID iDs

Ricciardi, Lorenzo A. ORCID logoORCID: https://orcid.org/0000-0002-0895-7961 and Vasile, Massimiliano ORCID logoORCID: https://orcid.org/0000-0001-8302-6465;
CORE (COnnecting REpositories)