Social group optimization (SGO), a populationbased optimization technique is proposed in this paper. It is inspired from the concept of social behavior of human toward solving a complex problem. The concept and the mathematical formulation of SGO algorithm is explained in this paper with a flowchart. To judge the effectiveness of SGO, extensive experiments have been conducted on number of different unconstrained benchmark functions as well as standard numerical benchmark functions taken from the IEEE Congress on Evolutionary Computation 2005 competition. Performance comparisons are made between state-of-theart optimization techniques, like GA, PSO, DE, ABC and its variants, and the recently developed TLBO. The investigational outcomes show that the proposed social group optimization outperforms all the investigated optimization techniques in computational costs and also provides optimal solutions for most of the functions considered in our work. The proposed technique is found to be very simple and straightforward to implement as well. It is believed that SGO will supplement the group of effective and efficient optimization techniques in the population-based category and give researchers wide scope to choose this in their respective applications.

No abstract

Abstract.A new efficient optimization method, called 'Teaching-LearningBased Optimization (TLBO)', has been proposed very recently for the optimization of mechanical design problems. This paper proposes a new approach to using TLBO to cluster data. It is shown how TLBO can be used to find the centroids of a user specified number of clusters. The new TLBO algorithms are evaluated on some datasets and compared to the performance of K-means and PSO clustering. Results show that TLBO clustering techniques have much potential.

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.

No abstract

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.

No abstract

No abstract