Academic Journal
On the automatic design of multi-objective particle swarm optimizers: experimentation and analysis
| Τίτλος: | On the automatic design of multi-objective particle swarm optimizers: experimentation and analysis |
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| Συγγραφείς: | Antonio J. Nebro, Manuel López‐Ibáñez, José García-Nieto, Carlos A. Coello Coello |
| Πηγή: | RIUMA. Repositorio Institucional de la Universidad de Málaga Universidad de Málaga |
| Στοιχεία εκδότη: | Springer Science and Business Media LLC, 2023. |
| Έτος έκδοσης: | 2023 |
| Θεματικοί όροι: | Artificial intelligence, Calidad total, Swarm behaviour, Systems engineering, Task (project management), Algoritmos computacionales, Engineering, Artificial Intelligence, Computer security, Machine learning, Swarm Intelligence Optimization Algorithms, Key (lock), Automatic algorithm design, Global Optimization, Multi-Objective Optimization, Particle swarm optimization, Optimization Applications, Computer science, Optimización matemática, Multi-objective optimization, Benchmarking, Computational Theory and Mathematics, Particle Swarm Optimization, Application of Genetic Programming in Machine Learning, Computer Science, Physical Sciences, Multiobjective Optimization in Evolutionary Algorithms |
| Περιγραφή: | Research in multi-objective particle swarm optimizers (MOPSOs) progresses by proposing one new MOPSO at a time. In spite of the commonalities among different MOPSOs, it is often unclear which algorithmic components are crucial for explaining the performance of a particular MOPSO design. Moreover, it is expected that different designs may perform best on different problem families and identifying a best overall MOPSO is a challenging task. We tackle this challenge here by: (1) proposing AutoMOPSO, a flexible algorithmic template for designing MOPSOs with a design space that can instantiate thousands of potential MOPSOs; and (2) searching for good-performing MOPSO designs given a family of training problems by means of an automatic configuration tool (irace). We apply this automatic design methodology to generate a MOPSO that significantly outperforms two state-of-the-art MOPSOs on four well-known bi-objective problem families. We also identify the key design choices and parameters of the winning MOPSO by means of ablation. AutoMOPSO is publicly available as part of the jMetal framework. |
| Τύπος εγγράφου: | Article Other literature type |
| Γλώσσα: | English |
| ISSN: | 1935-3820 1935-3812 |
| DOI: | 10.1007/s11721-023-00227-2 |
| DOI: | 10.60692/d71jd-0md05 |
| DOI: | 10.60692/r5a45-v5v83 |
| DOI: | 10.60692/m9tfx-gx987 |
| DOI: | 10.60692/4zcmp-07m45 |
| Σύνδεσμος πρόσβασης: | https://hdl.handle.net/10630/27853 |
| Rights: | CC BY CC BY NC ND |
| Αριθμός Καταχώρησης: | edsair.doi.dedup.....da6fea56ea78ef34ac3ad59aec5757cc |
| Βάση Δεδομένων: | OpenAIRE |
| ISSN: | 19353820 19353812 |
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| DOI: | 10.1007/s11721-023-00227-2 |