A Study on SVM Based on the Weighted Elitist Teaching-Learning-Based Optimization and Application in the Fault Diagnosis of Chemical Process

Abstract

Teaching-Learning-Based Optimization (TLBO) is a new swarm intelligence optimization algorithm that simulates the class learning process. According to such problems of the traditional TLBO as low optimizing efficiency and poor stability, this paper proposes an improved TLBO algorithm mainly by introducing the elite thought in TLBO and adopting different inertia weight decreasing strategies for elite and ordinary individuals of the teacher stage and the student stage. In this paper, the validity of the improved TLBO is verified by the optimizations of several typical test functions and the SVM optimized by the weighted elitist TLBO is used in the diagnosis and classification of common failure data of the TE chemical process. Compared with the SVM combining other traditional optimizing methods, the SVM optimized by the weighted elitist TLBO has a certain improvement in the accuracy of fault diagnosis and classification.

Production Wastage Avoidance Using Modified Multi-Objective Teaching Learning Based Optimization Embedded With Refined Learning Scheme

“…It is also noteworthy that the strategy of assigning same mean position that represents the mainstream knowledge of population for all learners is contradictory with real-world scenario of teaching and learning because each learner supposed to have slightly different perception on the mainstream knowledge of classroom [42]. In addition, it is observed that the learner phases of some TLBO variants [9,10,15,16,19,20,23,30,[36][37][38][39] did not accurately reflect the actual scenario of peer interaction in classroom. Some of these TLBO variants also only allowed each learner to interact with same peer learner in all dimensions during the learner phase.…”

“…It promoted global search at early stage of search process and emphasized the local search in latter stage. Similarly, a weighted elitist TLBO (WETLBO) was proposed in [10] to search for the best hyperparameters of support vector machine (SVM) for classifying and diagnosing the faulty data collected from chemical process. Varying population size in triangular form (VTTLBO) was introduced by [11] in which varying population size was attempted to reduce the computing cost.…”

“…• The works by [9,10] had focused on applying various parameter adaptation strategies to adjust the exploration and exploitation strengths of TLBO variants. These approaches tend to introduce excessive amounts of control parameters that are difficult to be tuned.…”

Optimal Training of Feedforward Neural Networks Using Teaching-Learning-Based Optimization with Modified Learning Phases

“…Furthermore, the incorporation of parameter adaptation strategy is worth to be investigated to enhance the robustness of TLBO algorithm in addressing optimization problems. The concepts of inertia weight and acceleration coefficients were incorporated into the enhanced TLBO [41] to determine the effect of previous learner and learning step size, respectively, aiming to achieve better learning efficiency of algorithm. An adaptive weight factor was introduced in TLBO variant [42] to emphasize the explorative and exploitative behavior at the early and later stage of optimization, respectively.…”

Review of applications of TLBO algorithm and a tutorial for beginners to solve the unconstrained and constrained optimization problems

“…140. Cao and Luo (2015) A study on SVM based on the weighted elitist TLBO was carried out and applied in the fault diagnosis of a chemical process.…”