Search Trajectory Networks of Population-Based Algorithms in Continuous Spaces
We introduce search trajectory networks (STNs) as a tool to analyse and visualise the behaviour of population-based algorithms in continuous spaces. Inspired by local optima networks (LONs) that model the global structure of search spaces, STNs model the search trajectories of algorithms. Unlike LON...
| Autores: | , , |
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| Tipo de recurso: | otro |
| Estado: | Versión aceptada para publicación |
| Fecha de publicación: | 2020 |
| País: | España |
| Institución: | Consejo Superior de Investigaciones Científicas (CSIC) |
| Repositorio: | DIGITAL.CSIC. Repositorio Institucional del CSIC |
| OAI Identifier: | oai:digital.csic.es:10261/235425 |
| Acceso en línea: | http://hdl.handle.net/10261/235425 https://doi.org/10.1007/978-3-030-43722-0_5 |
| Access Level: | acceso abierto |
| Palabra clave: | Continuous optimization Local optima networks Metaheuristics behaviour Search trajectory networks |
| Sumario: | We introduce search trajectory networks (STNs) as a tool to analyse and visualise the behaviour of population-based algorithms in continuous spaces. Inspired by local optima networks (LONs) that model the global structure of search spaces, STNs model the search trajectories of algorithms. Unlike LONs, the nodes of the network are not restricted to local optima but instead represent a given state of the search process. Edges represent search progression between consecutive states. This extends the power and applicability of network-based models to understand heuristic search algorithms. We extract and analyse STNs for two well-known population-based algorithms: particle swarm optimisation and differential evolution when applied to benchmark continuous optimisation problems. We also offer a comparative visual analysis of the search dynamics in terms of merged search trajectory networks. |
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