Interacted Multiple Ant Colonies Optimization (IMACO) is a newly proposed framework. In this framework several colonies of artificial ants are utilized. These colonies are working cooperatively to solve an optimization problem using some interaction technique. Exploration technique is doing an essential job in this framework. This technique is responsible for directing the activity of utilized colonies towards the different parts of the huge search space. This paper describes the newly proposed IMACO framework and proposes an effective exploration technique. Computational tests show that the new exploration technique can furthermore improve the IMACO performance. These tests also show the capability of IMACO to outperform other well known ant algorithms like ant colony system and max-min ant system. � 2010 IEEE.

Data mining (DM) is the extraction of hidden predictive information from large databases that has becoming a powerful new technology with great potential to help companies to focus on the most important information in their data warehouses. A predictive model makes a prediction about values of data using known results found from historical data where the best possible outcome based on the previous data is derived. Telekom Malaysia Berhad (TM) is Malaysia's premier communications provider that provides the digital backbone and communication facilities. Direct Exchange Line (DEL) is one of its core telephony services that handle large volume and variety of data in its daily operations. Therefore, it is hard to reveal knowledge structures that can guide decisions in conditions of limited certainty. The main objective of this study is to identify the most appropriate DM techniques (logistic regression, decision trees and neural networks) for predicting DEL growth based on five physical attributes constitute of 672 instances leading to a target (either increase or decrease). The finding is important especially in assisting the prediction of DEL growth in Telekom, thus leading on gaining better understanding on the future of the market based on the current and previous situation. � 2010 IEEE.

Interacted Multiple Ant Colonies Optimization (IMACO) is a newly proposed framework. Pheromone evaluation mechanism is playing a central role in this framework. This paper describes the newly proposed IMACO framework and proposes a more effective pheromone evaluation mechanism. Computational tests show that the new pheromone evaluation mechanism can furthermore improve the IMACO performance. These tests also show the capability of IMACO to outperform other well known ant algorithms like ant colony system and max-min ant system.