Abstract
This paper proposes a novel surrogate-model-based multi-objective evolutionary algorithm, which is called Multi-objective Bayesian Optimization Algorithm based on Decomposition (MOBO/D). In this algorithm, a multi-objective problem is decomposed into several subproblems which will be solved simultaneously. MOBO/D builds Gaussian process model for each objective to learn the optimization surface, and defines utility function for each sub problem to guide the searching process. At each generation, MOEA/D algorithm is called to locate a set of candidate solutions which maximize all utility functions respectively, and a subset of those candidate solutions is selected for parallel batch evaluation. Experimental study on different test instances validates that MOBOID can efficiently solve expensive multi-objective problems in parallel. The performance of MOBOID is also better than several classical expensive optimization methods.
| Original language | English |
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| Title of host publication | 2017 IEEE Congress on Evolutionary Computation (CEC) Proceedings |
| Publisher | IEEE |
| Pages | 1343-1349 |
| Number of pages | 7 |
| ISBN (Electronic) | 9781509046010 |
| ISBN (Print) | 9781509046027 |
| DOIs | |
| Publication status | Published - Jul 2017 |
| Externally published | Yes |
| Event | 2017 IEEE Congress on Evolutionary Computation, CEC 2017 - Donostia-San Sebastian, Spain Duration: 5 Jun 2017 → 8 Jun 2017 |
Conference
| Conference | 2017 IEEE Congress on Evolutionary Computation, CEC 2017 |
|---|---|
| Country/Territory | Spain |
| City | Donostia-San Sebastian |
| Period | 5/06/17 → 8/06/17 |
Funding
This work was supported by the National Natural Science Foundation of China under Grants 61473241, Hong Kong RGC General Research Fund(GRF) 9042038 (CityU 11205314), and a grant from ANR/RCC Joint Research Scheme sponsored by the Research Grants Council of the Hong Kong Special Administrative Region, China and France National Research Agency (Project No. A-CityUlOlIl6).