Elitist teaching-learning-based optimization (ETLBO) with higher-order Jordan Pi-sigma neural network: a comparative performance analysis

Abstract

This paper presents the performance analysis of a newly developed elitist teaching-learning-based optimization algorithm applied with an efficient higher-order Jordan Pi-sigma neural network (JPSNN) for real-world data classification. Teaching-learning-based optimization (TLBO) algorithm is a recent metaheuristic, which is inspired through the teaching and learning process of both teacher and learner. As compared to other algorithms, it is efficient and robust due to its non-controlling parameter adjustments feature. Elitist TLBO is an improved version of TLBO with the addition of elitist solutions, which makes it more efficient. During the experiment, first the TLBO and then ETLBO algorithm are applied with only Pi-sigma neural network and its performance has been compared with other methods such as GA and PSO. Then, the ETLBO algorithm is applied with JPSNN and found better results over other methods. The proposed method has been tested with real-world benchmark datasets considered from UCI machine learning repository, and the performance has been compared with all seven approaches along with other HONN to prove the effectiveness of the method. Simulation results and statistical analysis show the superiority in the performance of the proposed approach as well as prove the potentiality over other existing approaches.