Teaching–learning-based optimization: A novel method for constrained mechanical design optimization problems

A new efficient optimization method, called ‘Teaching–Learning-Based Optimization (TLBO)’, is proposed in this paper for the optimization of mechanical design problems. This method works on the effect of influence of a teacher on learners. Like other nature-inspired algorithms, TLBO is also a popula...

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Published in	Computer aided design Vol. 43; no. 3; pp. 303 - 315
Main Authors	Rao, R.V., Savsani, V.J., Vakharia, D.P.
Format	Journal Article
Language	English
Published	Elsevier Ltd 01.03.2011
Subjects	Algorithms Benchmarking Constrained benchmark functions Constraints Design optimization Learning Mathematical models Mechanical design optimization Optimization Teachers Teaching–learning-based optimization Constrained benchmark functions Teaching–learning-based optimization Mechanical design optimization
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Abstract	A new efficient optimization method, called ‘Teaching–Learning-Based Optimization (TLBO)’, is proposed in this paper for the optimization of mechanical design problems. This method works on the effect of influence of a teacher on learners. Like other nature-inspired algorithms, TLBO is also a population-based method and uses a population of solutions to proceed to the global solution. The population is considered as a group of learners or a class of learners. The process of TLBO is divided into two parts: the first part consists of the ‘Teacher Phase’ and the second part consists of the ‘Learner Phase’. ‘Teacher Phase’ means learning from the teacher and ‘Learner Phase’ means learning by the interaction between learners. The basic philosophy of the TLBO method is explained in detail. To check the effectiveness of the method it is tested on five different constrained benchmark test functions with different characteristics, four different benchmark mechanical design problems and six mechanical design optimization problems which have real world applications. The effectiveness of the TLBO method is compared with the other population-based optimization algorithms based on the best solution, average solution, convergence rate and computational effort. Results show that TLBO is more effective and efficient than the other optimization methods for the mechanical design optimization problems considered. This novel optimization method can be easily extended to other engineering design optimization problems. ► A novel optimization method, ‘Teaching–Learning-Based Optimization’, is proposed. ► Effectiveness of the algorithm is tested on many constrained benchmark problems. ► Results show better performance over other nature-inspired optimization methods. ► The method involves less computational effort for large scale problems. ► The method can be used for engineering design optimization applications.
AbstractList	A new efficient optimization method, called ‘Teaching–Learning-Based Optimization (TLBO)’, is proposed in this paper for the optimization of mechanical design problems. This method works on the effect of influence of a teacher on learners. Like other nature-inspired algorithms, TLBO is also a population-based method and uses a population of solutions to proceed to the global solution. The population is considered as a group of learners or a class of learners. The process of TLBO is divided into two parts: the first part consists of the ‘Teacher Phase’ and the second part consists of the ‘Learner Phase’. ‘Teacher Phase’ means learning from the teacher and ‘Learner Phase’ means learning by the interaction between learners. The basic philosophy of the TLBO method is explained in detail. To check the effectiveness of the method it is tested on five different constrained benchmark test functions with different characteristics, four different benchmark mechanical design problems and six mechanical design optimization problems which have real world applications. The effectiveness of the TLBO method is compared with the other population-based optimization algorithms based on the best solution, average solution, convergence rate and computational effort. Results show that TLBO is more effective and efficient than the other optimization methods for the mechanical design optimization problems considered. This novel optimization method can be easily extended to other engineering design optimization problems. ► A novel optimization method, ‘Teaching–Learning-Based Optimization’, is proposed. ► Effectiveness of the algorithm is tested on many constrained benchmark problems. ► Results show better performance over other nature-inspired optimization methods. ► The method involves less computational effort for large scale problems. ► The method can be used for engineering design optimization applications. A new efficient optimization method, called 'Teaching-Learning-Based Optimization (TLBO)', is proposed in this paper for the optimization of mechanical design problems. This method works on the effect of influence of a teacher on learners. Like other nature-inspired algorithms, TLBO is also a population-based method and uses a population of solutions to proceed to the global solution. The population is considered as a group of learners or a class of learners. The process of TLBO is divided into two parts: the first part consists of the 'Teacher Phase' and the second part consists of the 'Learner Phase'. 'Teacher Phase' means learning from the teacher and 'Learner Phase' means learning by the interaction between learners. The basic philosophy of the TLBO method is explained in detail. To check the effectiveness of the method it is tested on five different constrained benchmark test functions with different characteristics, four different benchmark mechanical design problems and six mechanical design optimization problems which have real world applications. The effectiveness of the TLBO method is compared with the other population-based optimization algorithms based on the best solution, average solution, convergence rate and computational effort. Results show that TLBO is more effective and efficient than the other optimization methods for the mechanical design optimization problems considered. This novel optimization method can be easily extended to other engineering design optimization problems.
Author	Savsani, V.J. Vakharia, D.P. Rao, R.V.
Author_xml	– sequence: 1 givenname: R.V. surname: Rao fullname: Rao, R.V. email: ravipudirao@gmail.com – sequence: 2 givenname: V.J. surname: Savsani fullname: Savsani, V.J. – sequence: 3 givenname: D.P. surname: Vakharia fullname: Vakharia, D.P.
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PublicationDateYYYYMMDD	2011-03-01
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PublicationDecade	2010
PublicationTitle	Computer aided design
PublicationYear	2011
Publisher	Elsevier Ltd
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Snippet	A new efficient optimization method, called ‘Teaching–Learning-Based Optimization (TLBO)’, is proposed in this paper for the optimization of mechanical design... A new efficient optimization method, called 'Teaching-Learning-Based Optimization (TLBO)', is proposed in this paper for the optimization of mechanical design...
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SubjectTerms	Algorithms Benchmarking Constrained benchmark functions Constraints Design optimization Learning Mathematical models Mechanical design optimization Optimization Teachers Teaching–learning-based optimization
Title	Teaching–learning-based optimization: A novel method for constrained mechanical design optimization problems
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