A multi-objective hyper-heuristic based on choice function

•A learning selection hyper-heuristic is proposed for multi-objective optimization.•A choice function utilized within the framework for multi-objective optimization.•Three MOEAs (NSGAII, SPEA2, and MOGA) are mixes and exploited their strengths.•The proposed method performs better than three MOEAs an...

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Veröffentlicht in:Expert systems with applications Jg. 41; H. 9; S. 4475 - 4493
Hauptverfasser: Maashi, Mashael, Özcan, Ender, Kendall, Graham
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Amsterdam Elsevier Ltd 01.07.2014
Elsevier
Schlagworte:
Algorithmics. Computability. Computer arithmetics
Amalgams
Applied sciences
Artificial intelligence
Computer science; control theory; systems
Decision theory. Utility theory
Evolutionary algorithm
Exact sciences and technology
Heuristic
Hyper-heuristic
Learning
Learning and adaptive systems
Low level
Mathematical analysis
Mathematical models
Metaheuristic
Multi-objective optimization
Operational research and scientific management
Operational research. Management science
Optimization
Searching
Theoretical computing
Metaheuristic
Multi-objective optimization
Evolutionary algorithm
Hyper-heuristic
Legged locomotion
Multiobjective programming
Selection function
Adaptive method
Optimization
Search algorithm
Walking
Vertebrata
Experimental result
Pisces
Heuristic method
Artificial intelligence
Metamodel
Mathematical programming
ISSN:0957-4174, 1873-6793
Online-Zugang:Volltext
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Zusammenfassung:•A learning selection hyper-heuristic is proposed for multi-objective optimization.•A choice function utilized within the framework for multi-objective optimization.•Three MOEAs (NSGAII, SPEA2, and MOGA) are mixes and exploited their strengths.•The proposed method performs better than three MOEAs and some other approaches.•The proposed method is tested on a generic benchmark and a real-world problem. Hyper-heuristics are emerging methodologies that perform a search over the space of heuristics in an attempt to solve difficult computational optimization problems. We present a learning selection choice function based hyper-heuristic to solve multi-objective optimization problems. This high level approach controls and combines the strengths of three well-known multi-objective evolutionary algorithms (i.e. NSGAII, SPEA2 and MOGA), utilizing them as the low level heuristics. The performance of the proposed learning hyper-heuristic is investigated on the Walking Fish Group test suite which is a common benchmark for multi-objective optimization. Additionally, the proposed hyper-heuristic is applied to the vehicle crashworthiness design problem as a real-world multi-objective problem. The experimental results demonstrate the effectiveness of the hyper-heuristic approach when compared to the performance of each low level heuristic run on its own, as well as being compared to other approaches including an adaptive multi-method search, namely AMALGAM.
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ISSN:0957-4174
1873-6793
DOI:10.1016/j.eswa.2013.12.050