Surrogate-Assisted Evolutionary Optimization of Large Problems
| Autor: | Yaochu Jin, Chaoli Sun, Handing Wang, Tinkle Chugh |
|---|---|
| Přispěvatelé: | Bartz-Beielstein, Thomas, Filipič, Bogdan, Korošec, Peter, Talbi, El-Ghazali |
| Rok vydání: | 2019 |
| Předmět: |
Mathematical optimization
Optimization algorithm optimisation Computer science Evolutionary algorithm Swarm behaviour evoluutiolaskenta 02 engineering and technology matemaattinen optimointi mathematical optimisation Decision variables Empirical research optimointi evolutionary computation 0202 electrical engineering electronic engineering information engineering Reference vector 020201 artificial intelligence & image processing |
| Zdroj: | High-Performance Simulation-Based Optimization ISBN: 9783030187637 High-Performance Simulation-Based Optimization |
| DOI: | 10.1007/978-3-030-18764-4_8 |
| Popis: | This chapter presents some recent advances in surrogate-assisted evolutionary optimization of large problems. By large problems, we mean either the number of decision variables is large, or the number of objectives is large, or both. These problems pose challenges to evolutionary algorithms themselves, constructing surrogates and surrogate management. To address these challenges, we proposed two algorithms, one called kriging-assisted reference vector guided evolutionary algorithm (K-RVEA) for many-objective optimization, and the other called cooperative swarm optimization algorithm (SA-COSO) for high-dimensional single-objective optimization. Empirical studies demonstrate that K-RVEA works well for many-objective problems having up to ten objectives, while SA-COSA outperforms the state-of-the-art algorithms on 200-dimensional single-objective test problems. peerReviewed |
| Databáze: | OpenAIRE |
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