An elitist teaching-learning-based optimization algorithm for solving complex constrained optimization problems

Abstract

Nature inspired population based algorithms is a research field which simulates different natural phenomena to solve a wide range of problems. Researchers have proposed several algorithms considering different natural phenomena. Teaching-Learning-based optimization (TLBO) is one of the recently proposed population based algorithms which simulates the teaching-learning process of the class room. This algorithm does not require any algorithm-specific control parameters. In this paper, elitism concept is introduced in the TLBO algorithm and its effect on the performance of the algorithm is investigated. The effects of common controlling parameters such as the population size and the number of generations on the performance of the algorithm are also investigated. The proposed algorithm is tested on 35 constrained benchmark functions with different characteristics and the performance of the algorithm is compared with that of other well known optimization algorithms. The proposed algorithm can be applied to various optimization problems of the industrial environment.

OpenMP Teaching-Learning Based Optimization Algorithm over Multi-Core System

“…Gonzá lez-Álvarez et al in [26] proposed Multi-objective TLBO (MO-TLBO) for solving Motif Discovery Problem (MDP) and solved a set of twelve biological instances belonging to different organisms. Rao and Patel introduced and investigated the effect of elitism on the performance of TLBO algorithm while solving complex constrained optimization problems [27] and unconstrained benchmark problems [28]. Population size and Number of generation, these parameter affects the performance of TLBO are also investigated.…”

Tuning of a PID Controller using Modified Dynamic Group based TLBO Algorithm

“…In the teacher phase, all the students learn from the teacher, whereas in the learner phase, students learn through the interaction between other students. More detailed description of original TLBO could refer to the paper [12][13][14][15][16][17][18]. In this section, more attention would be focused on the teaching-learning-based optimization with dynamic group strategy (DGS-TLBO).…”

“…Teaching-learning-based optimization (TLBO) is based on the philosophy of teaching and learning and it works on the effect of influence of a teacher on the output of learners in a class [14]. In this algorithm, the population is considered as a group of learners and different design variables related to each learner can be considered as different subjects.…”

Fault Detection Based on Multi-Scale Local Binary Patterns Operator and Improved Teaching-Learning-Based Optimization Algorithm

“…Rao and Patel have introduced the elite TLBO algorithm, in which, the worst learner is replaced by the elite learner after the learner phase [30]. If duplicate learners exist after the replacement, one randomly selected subject of the duplicate learner is modified into a random value.…”