A Novel Approach in Data Clustering using Population Based Optimization Algorithm to solve Economic Load Dispatch Problem
Journal Title: International Journal of Research in Computer and Communication Technology - Year 2014, Vol 3, Issue 2
Abstract
Computing the global optima of a functional has been extensively applied in a wide range of engineering applications. Nevertheless, it is well known they usually lack of effectiveness when dealing with complex nonlinear optimization problems. This paper presents a new clustering algorithm based on the mechanism analysis of Artificial Bee Colony (ABC) Algorithms. It is an optimization methodology for clustering problem which aims to obtain global optimal assignment by minimizing the objective function. Bee colonies exemplify a high level of intrinsic interdependence and coordination among its members, and algorithms inspired from the bee colonies have gained recent prominence in the field of swarm based metaheuristics. The ABC algorithm was recently developed, by simulating the minimalistic foraging model of honeybees in search of food sources, for solving realparameter, non-convex, and non-smooth optimization problems. The proposed algorithm combines finding global optimal solution to the objective function, fitness function and clusters with different size and density for suitable to multidimensional data named as Iteratively Optimized Artificial Bee Colony (IOABC) Algorithm. The quality of this approach is evaluated and then compared with the popular clustering method named k-means algorithm clustering technique. The proposed fitness learning mechanism with a weighted selection scheme helps to achieve a blending of explorative and exploitative behavior by enhancing both local and global searching ability of the algorithm. Here considering the problem for Economic Load Dispatch (ELD) for three generator problem for economic load based on the cost of fuel. It deals with complexity of finding the optimal solution for this real time cases. The work is to prove experimental results of our algorithm is an effective clustering technique and can be used to handle data’s with complex cluster sizes, densities and multiple dimensions.
Authors and Affiliations
S Krishna Kumar, E Srie Vidhya Janani
Keywords
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