K. Parvathi

A teaching learning based optimization based on orthogonal design for solving global optimization problems

In searching for optimal solutions, teaching learning based optimization (TLBO) (Rao et al. 2011a; Rao et al. 2012; Rao & Savsani 2012a) algorithms, has been shown powerful. This paper presents an, improved version of TLBO algorithm based on orthogonal design, and we call it OTLBO (Orthogonal Teaching Learning Based Optimization). OTLBO makes TLBO faster and more robust. It uses orthogonal design and generates an optimal offspring by a statistical optimal method. A new selection strategy is applied to decrease the number of generations and make the algorithm converge faster. We evaluate OTLBO to solve some benchmark function optimization problems with a large number of local minima. Simulations indicate that OTLBO is able to find the near-optimal solutions in all cases. Compared to other state-of-the-art evolutionary algorithms, OTLBO performs significantly better in terms of the quality, speed, and stability of the final solutions.

QoS Multicast Routing Using Teaching Learning Based Optimization

The QoS multicast routing problem is to find a multicast routing tree with minimal cost that can satisfy constraints such as bandwidth, delay. This problem is NP Complete. Hence, the problem is usually solved by heuristic or intelligence optimization. In this paper, we present a Teaching learning based optimization method to optimize the multicast tree. A fitness function is used to implement the constraints specified by the Quality of Service conditions. The experimental results dealt with relations between the number of nodes, edges in the input graph and convergence time, the optimal solution quality comparison with other evolutionary techniques. The results reveal that our algorithm performs better than the existing algorithms.

0-1 Integer Programming for Generation Maintenance Scheduling in Power Systems Based on Teaching Learning Based Optimization (TLBO)

This paper presents optimal solution of the unit maintenance scheduling problem in which the cost reduction is as important as reliability. The objective function of the algorithms used to address this problem, considers the effect of economy as well as reliability. Various constraints such as spinning reserve, duration of maintenance crew are being taken into account while dealing with such type of problems. In our work we apply the Teaching learning based optimization algorithm on a power system with six generating units. Numerical results reveal that the proposed algorithm can find better and faster solutions when compared to other heuristic or deterministic methods.

Rough set and teaching learning based optimization technique for optimal features selection

Rough set theory has been one of the most successful methods used for feature selection. However, this method is still not able to find optimal subsets. But it can be made to be optimal using different optimization techniques. This paper proposes a new feature selection method based on Rough Set theory with Teaching learning based optimization (TLBO). The proposed method is experimentally compared with other hybrid Rough Set methods such as Genetic Algorithm (GA), Particle Swarm Optimization (PSO) and Differential Evolution (DE) and the empirical results reveal that the proposed approach could be used for feature selection as this performs better in terms of finding optimal features and doing so in quick time.

Teaching learning based optimization for neural networks learning enhancement

Evolutionary computation is a collection of algorithms based on the evolution of a population towards a solution of certain problem. These algorithms can be used successfully in many applications requiring the optimization. These algorithms have been widely used to optimize the learning mechanism of classifiers, particularly on Artificial Neural Network (ANN) Classifier. Major disadvantages of ANN classifier are due to its slow convergence and always being trapped at the local minima. To overcome this problem, TLBO (Teaching learning based optimization) has been used to determine optimal value for learning mechanism. In this study, TLBO is chosen and applied to feed forward neural network to enhance the learning process. Two programs have developed, Differential Evolution Neural Network (DENN) and Particle Swarm Optimization with Neural Network (PSONN) to probe the impact of these methods on a Teaching learning based optimization with neural network (TLBONN) learning using various datasets. The results have revealed that TLBONN has given quite promising results in terms of smaller errors compared to PSONN and DENN.

Unsupervised feature selection using rough set and teaching learning-based optimisation

Feature selection is a valuable technique in data analysis for information preserving data reduction. This paper proposes to consider an information system without any decision attribute. The proposal is useful when we get unlabeled data, which contains only input information condition attributes but without decision class attribute. TLBO clustering algorithm is applied to cluster the given information. Decision table could be formulated using this clustered data as the decision variable. Then rough set and TLBO algorithms are applied for selecting features. The experiments are carried out on datasets of UCI machine repository and from the website http://www.ailab.si/orange/datasets.asp to analyse the performance study of our proposed approach with other approaches like genetic algorithm, particle swarm optimisation and differential evolution techniques. The results clearly reveal that our proposed approach outperforms other approaches investigated in this paper.

A Comparative Analysis of Results of Data Clustering with Variants of Particle Swarm Optimization

Particle Swarm Optimization (PSO) has been extensively studied, in recent past, for solving various engineering optimization problems. There have been many variants of PSO available in literatures. This paper presents a comparative analysis of few popular variants of PSO on the problem of data clustering. The investigated algorithms are evaluated on many real world datasets and few artificial datasets and clustering results are presented. Further, the results of statistical test on effectiveness of each investigated variants of PSO also demonstrated. The convergence characteristics of each variant are shown for different datasets. This study may be helpful to many researchers in choosing suitable PSO variants for their application.