Search trajectory networks: A tool for analysing and visualising the behaviour of metaheuristics

[EN]A large number of metaheuristics inspired by natural and social phenomena have been proposed inthe last few decades, each trying to be more powerful and innovative than others. However, thereis a lack of accessible tools to analyse, contrast and visualise the behaviour of metaheuristics whensolving optimisation problems. When the metaphors are stripped away, are these algorithms differentin their behaviour? To help to answer this question, we propose a data-driven, graph-based model,search trajectory networks(STNs) in order to analyse, visualise and directly contrast the behaviour ofdifferenttypesofmetaheuristics.Onestrengthofourapproachisthatitdoesnotrequireanyadditionalsampling or algorithmic methods. Instead, the models are constructed from data gathered while themetaheuristics are solving the optimisation problems. We present our methodology, and considerin detail two case studies covering both continuous and combinatorial optimisation. In terms ofmetaheuristics,ourcasestudiescoverthemaincurrentparadigms:evolutionary,swarm,andstochasticlocal search approaches.Christian Blum was funded by project CI-SUSTAIN of the SpanIsh Ministry of Science and Innovation (PID2019-104156GB I00). Katherine Malan was funded by the National Research Foundation of South Africa (Grant Number: 120837).Peer reviewe