Abstract
Population based incremental learning algorithms and selection hyper-heuristics are highly adaptive methods which can handle different types of dynamism that may occur while a given problem is being solved. In this study, we present an approach based on a multi-population framework hybridizing these methods to solve dynamic environment problems. A key feature of the hybrid approach is the utilization of offline and online learning methods at successive stages. The performance of our approach along with the influence of different heuristic selection methods used within the selection hyper-heuristic is investigated over a range of dynamic environments produced by a well known benchmark generator as well as a real world problem, referred to as the Unit Commitment Problem. The empirical results show that the proposed approach using a particular hyper-heuristic outperforms some of the best known approaches in literature on the dynamic environment problems dealt with.
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- EDA:
-
Estimation of distribution algorithms
- HH-EDA2:
-
Hyper-heuristic based dual population EDA
- PBIL:
-
Population based incremental learning
- PBIL2:
-
Dual population PBIL
- PBILr:
-
PBIL with restart
- PBIL2r:
-
PBIL2 with restart
- MPBILr:
-
Memory-based PBIL with restart
- MPBIL2r:
-
Dual population memory-based PBIL with restart
- Sentinel8:
-
Sentinel-based genetic algorithm with 8 sentinels
- Sentinel16:
-
Sentinel-based genetic algorithm with 16 sentinels
- DUF:
-
Decomposable unitation-based function
- MW:
-
Megawatt
- UCP:
-
Unit commitment problem
- CL:
-
Cycle length
- HF:
-
High frequency
- HS:
-
High severity
- LF:
-
Low frequency
- LS:
-
Low severity
- MF:
-
Medium frequency
- MS:
-
Medium severity
- VHS:
-
Very high severity
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Acknowledgments
This work is supported in part by the EPSRC, grant EP/F033214/1 (The LANCS Initiative Postdoctoral Training Scheme) and Berna Kiraz is supported by the TUBITAK 2211-National Scholarship Programme for PhD students.
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Communicated by G. Acampora.
A preliminary version of this study was presented at UKCI 2012: 12th Annual Workshop on Computational Intelligence.
Appendix: System data
Appendix: System data
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Uludağ, G., Kiraz, B., Etaner-Uyar, A.Ş. et al. A hybrid multi-population framework for dynamic environments combining online and offline learning. Soft Comput 17, 2327–2348 (2013). https://doi.org/10.1007/s00500-013-1094-7
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DOI: https://doi.org/10.1007/s00500-013-1094-7