New Methods in Ant Colony Optimization Using Multiple Foraging Approach to Increase Stability

Abstract

With an ever-increasing need for firms to analyze data being collected from various sources such as the Internet and other forms of e-commerce, there is a greater need for more improved segmentation techniques for differentiated marketing programs aimed at maximizing revenues and profitability. K-means clustering is a popular technique for segmenting large data sets. Recently, algorithms mimicking the behavior of ant colonies have been shown to bring significant improvements to the K-means clustering algorithm and other methods of knowledge discovery in databases. These techniques were developed by imitating the behavior of real ants for finding the shortest path from their nests to the food source. This chapter represents an application that aims to cluster a data set by means of an ant colony optimization algorithm. It also increases the working performance of this algorithm used for solving the data clustering problem by proposing a multipronged foraging approach, resulting in the globally optimal solution and showing the advantage in the performance due to suggested technique. A limitation of this study is the generalizability of the results to other data sources as this algorithm was only tested in production on financial services data. Further research is necessary on additional sources of data from other domains.

This chapter contains contributions from Avanti George, Madras School of Economics, Chennai, India.