TY - GEN
T1 - Extending the speed-constrained multi-objective PSO (SMPSO) with reference point based preference articulation
AU - Nebro, Antonio J.
AU - Durillo, Juan J.
AU - García-Nieto, José
AU - Barba-González, Cristóbal
AU - Del Ser, Javier
AU - Coello Coello, Carlos A.
AU - Benítez-Hidalgo, Antonio
AU - Aldana-Montes, José F.
N1 - Publisher Copyright:
© Springer Nature Switzerland AG 2018.
PY - 2018
Y1 - 2018
N2 - 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.
AB - 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.
KW - Decision making
KW - Multi-objective optimization
KW - Reference point
KW - SMPSO
UR - https://www.scopus.com/pages/publications/85053619137
U2 - 10.1007/978-3-319-99253-2_24
DO - 10.1007/978-3-319-99253-2_24
M3 - Conference contribution
AN - SCOPUS:85053619137
SN - 9783319992525
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 298
EP - 310
BT - Parallel Problem Solving from Nature – PPSN XV - 15th International Conference, 2018, Proceedings
A2 - Fonseca, Carlos M.
A2 - Lourenco, Nuno
A2 - Machado, Penousal
A2 - Paquete, Luis
A2 - Whitley, Darrell
A2 - Auger, Anne
PB - Springer Verlag
T2 - 15th International Conference on Parallel Problem Solving from Nature, PPSN 2018
Y2 - 8 September 2018 through 12 September 2018
ER -