Abstract
The Speed-constrained Multi-objective PSO (SMPSO) is an approach featuring an external bounded archive to store non-dominated solutions found during the search and out of which leaders that guide the particles are chosen. Here, we introduce SMPSO/RP, an extension of SMPSO based on the idea of reference point archives. These are external archives with an associated reference point so that only solutions that are dominated by the reference point or that dominate it are considered for their possible addition. SMPSO/RP can manage several reference point archives, so it can effectively be used to focus the search on one or more regions of interest. Furthermore, the algorithm allows interactively changing the reference points during its execution. Additionally, the particles of the swarm can be evaluated in parallel. We compare SMPSO/RP with respect to three other reference point based algorithms. Our results indicate that our proposed approach outperforms the other techniques with respect to which it was compared when solving a variety of problems by selecting both achievable and unachievable reference points. A real-world application related to civil engineering is also included to show up the real applicability of SMPSO/RP.
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Acknowledgement
This work has been partially funded by Grants TIN2017-86049-R (Spanish Ministry of Education and Science) and P12-TIC-1519 (Plan Andaluz de Investigación, Desarrollo e Innovación). Cristóbal Barba-González is supported by Grant BES-2015-072209 (Spanish Ministry of Economy and Competitiveness). José García-Nieto is the recipient of a Post-Doctoral fellowship of “Captación de Talento para la Investigación” Plan Propio at Universidad de Málaga. Javier Del Ser thanks the Basque Government for its funding support through the EMAITEK program. Carlos A. Coello Coello is supported by CONACyT project no. 221551.
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Nebro, A.J. et al. (2018). Extending the Speed-Constrained Multi-objective PSO (SMPSO) with Reference Point Based Preference Articulation. In: Auger, A., Fonseca, C., Lourenço, N., Machado, P., Paquete, L., Whitley, D. (eds) Parallel Problem Solving from Nature – PPSN XV. PPSN 2018. Lecture Notes in Computer Science(), vol 11101. Springer, Cham. https://doi.org/10.1007/978-3-319-99253-2_24
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