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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Published in:	Expert systems with applications Vol. 41; no. 9; pp. 4475 - 4493
Main Authors:	Maashi, Mashael, Özcan, Ender, Kendall, Graham
Format:	Journal Article
Language:	English
Published:	Amsterdam Elsevier Ltd 01.07.2014 Elsevier
Subjects:	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
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Abstract	•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.
AbstractList	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. •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.
Author	Kendall, Graham Özcan, Ender Maashi, Mashael
Author_xml	– sequence: 1 givenname: Mashael surname: Maashi fullname: Maashi, Mashael email: psxmm3@exmail.nottingham.ac.uk, m.maashi@gmail.com organization: Automated Scheduling, Optimisation and Planning Research Group, University of Nottingham, Department of Computer Science, Jubilee Campus, Wollaton Road, Nottingham NG8 1BB, UK – sequence: 2 givenname: Ender surname: Özcan fullname: Özcan, Ender email: ender.ozcan@nottingham.ac.uk organization: Automated Scheduling, Optimisation and Planning Research Group, University of Nottingham, Department of Computer Science, Jubilee Campus, Wollaton Road, Nottingham NG8 1BB, UK – sequence: 3 givenname: Graham surname: Kendall fullname: Kendall, Graham email: graham.kendall@nottingham.edu.my organization: Automated Scheduling, Optimisation and Planning Research Group, University of Nottingham, Department of Computer Science, Jubilee Campus, Wollaton Road, Nottingham NG8 1BB, UK
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Keywords	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
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Snippet	•A learning selection hyper-heuristic is proposed for multi-objective optimization.•A choice function utilized within the framework for multi-objective... Hyper-heuristics are emerging methodologies that perform a search over the space of heuristics in an attempt to solve difficult computational optimization...
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SubjectTerms	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
Title	A multi-objective hyper-heuristic based on choice function
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