Abstract
Teaching-Learning-Based Optimization (TLBO) is recently being used as a new, reliable, accurate and robust optimization technique scheme for global optimization over continuous spaces [1]. This paper presents an, improved version of TLBO algorithm, called the Weighted Teaching-Learning-Based Optimization (WTLBO). This algorithm uses a parameter in TLBO algorithm to increase convergence rate. Performance comparisons of the proposed method are provided against the original TLBO and some other very popular and powerful evolutionary algorithms. The weighted TLBO (WTLBO) algorithm on several benchmark optimization problems shows a marked improvement in performance over the traditional TLBO and other algorithms as well.
Highlights
In evolutionary algorithms the convergence rate of the algorithm is given prime importance for solving an optimization problem
Following are the notations used for describing the Teaching-Learning-Based Optimization (TLBO): N: number of learners in a class i.e. “class size”; D: number of courses offered to the learners; MAXIT: maximum number of allowable iterations
We used 20 benchmark problems in order to test the performance of the Particle swarm Optimizations (PSO), Differential Evolution (DE), TLBO and the weighted TLBO (WTLBO) algorithms
Summary
In evolutionary algorithms the convergence rate of the algorithm is given prime importance for solving an optimization problem. The ability of the algorithm to obtain the global optima value is one aspect and the faster convergence is the other aspect It is studied in the evolutionary techniques literature that there are few good techniques, often achieve global optima results but at the cost of the convergence speed. The inclusion of this parameter is found bettering the convergence speed of TLBO, even providing better results for few problems. The performance of WTLBO for solving global function optimization problems is compared with basic TLBO and other evolutionary techniques. It can be revealed from the results analysis that our proposed approach outperforms all approaches investigated in this paper.
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