Teaching-Learning-Based Optimization with Learning Enthusiasm Mechanism and Its Application in Chemical Engineering

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.

Optimized localization of sensor nodes in 3D WSNs using modified learning enthusiasm‐based teaching learning based optimization algorithm

“…where x n_opt is the best solution in the neighbourhood of x t i , and u, v ∈rand() are the scaling factors. The updated solution performs the teacher phase using LNS is given by Comparison with FPA [38], GWO [13], CS [39], FDO [40], SCA [41], TLBO [36], and LebTLBO [12] is done to check the effectiveness of its proposed variant. For every algorithm, a total of 500 iterations are performed using 30 agents.…”

“…On the other hand, learners with low learning enthusiasm have relatively little chance of learning from others. This research, motivated by this behaviour, will bring to the TLBO the notion of the mechanism of learning enthusiasm to suggest a novel strategy called LebTLBO [12].…”

Parametric optimization of fused deposition modeling using learning enthusiasm enabled teaching learning based algorithm

“…The first stage is employed to increase the search capacity, in order to improve the quality of poor learners using poor student tutoring phase. Every learner has the same ability to acquire information from others in basic TLBO [23]. However, LebTLBO is guided by a process of real-world learning excitement, where each learner has different abilities and excitement or eagerness to learn.…”

Efficient Localized Data Aggregation of IoT Devices and its Application in Agriculture

“…For every step, the particles had been evaluated using a fitness function with their speed and position updated. This algorithm stopped at the time a quasi-optimal solution was identified or maximum number of iterations [17].…”