Abstract
Teaching-learning-based optimization (TLBO) is a population-based metaheuristic search algorithm inspired by the teaching and learning process in a classroom. It has been successfully applied to many scientific and engineering applications in the past few years. In the basic TLBO and most of its variants, all the learners have the same probability of getting knowledge from others. However, in the real world, learners are different, and each learner’s learning enthusiasm is not the same, resulting in different probabilities of acquiring knowledge. Motivated by this phenomenon, this study introduces a learning enthusiasm mechanism into the basic TLBO and proposes a learning enthusiasm based TLBO (LebTLBO). In the LebTLBO, learners with good grades have high learning enthusiasm, and they have large probabilities of acquiring knowledge from others; by contrast, learners with bad grades have low learning enthusiasm, and they have relative small probabilities of acquiring knowledge from others. In addition, a poor student tutoring phase is introduced to improve the quality of the poor learners. The proposed method is evaluated on the CEC2014 benchmark functions, and the computational results demonstrate that it offers promising results compared with other efficient TLBO and non-TLBO algorithms. Finally, LebTLBO is applied to solve three optimal control problems in chemical engineering, and the competitive results show its potential for real-world problems.
Highlights
In recent years, many real-world problems have become extremely complex and are difficult to solve using classic analytical optimization algorithms
A poor student tutoring phase is introduced to improve the quality of the poor learners
(1) Begin (2) Sort all the learners from best to worst; (3) The learners ranked at the bottom 10 percent are considered as the poor students. (4) for each poor learner xi (5) Randomly select a student xT ranked at the top 50 percent (6) Update learner xi according to Eq (6); (7) Evaluate the new learner xi,new; (8) Accept the new learner xi,new if it is better than the old one xi,old; (9) end for (10) end Algorithm 4: Poor student tutoring phase
Summary
Many real-world problems have become extremely complex and are difficult to solve using classic analytical optimization algorithms. Metaheuristic search (MS) algorithms have shown more favorable performance on nonconvex and nondifferentiable problems, resulting in the development of various MS algorithms for difficult realworld problems. Most of these MS algorithms are natureinspired, and several of the prominent algorithms include genetic algorithms (GA) [1], evolution strategies (ES) [2], differential evolution (DE) [3], particle swarm optimization (PSO) [4, 5], harmony search (HS) [6], and biogeographybased optimization (BBO) [7, 8]. TLBO has been successfully applied to many scientific and Journal of Applied Mathematics engineering fields, such as neural network training [17], power system dispatch [18], and production scheduling [19]
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