Hyperbox clustering with Ant Colony Optimization (HACO) method and its application to medical risk profile recognition

Improved Ant Colony Clustering Algorithm and Its Performance Study

Clustering analysis is used in many disciplines and applications; it is an important tool that descriptively identifies homogeneous groups of objects based on attribute values. The ant colony clustering algorithm is a swarm-intelligent method used for clustering problems that is inspired by the behavior of ant colonies that cluster their corpses and sort their larvae. A new abstraction ant colony clustering algorithm using a data combination mechanism is proposed to improve the computational efficiency and accuracy of the ant colony clustering algorithm. The abstraction ant colony clustering algorithm is used to cluster benchmark problems, and its performance is compared with the ant colony clustering algorithm and other methods used in existing literature. Based on similar computational difficulties and complexities, the results show that the abstraction ant colony clustering algorithm produces results that are not only more accurate but also more efficiently determined than the ant colony clustering algorithm and the other methods. Thus, the abstraction ant colony clustering algorithm can be used for efficient multivariate data clustering.

Seven-spot ladybird optimization: a novel and efficient metaheuristic algorithm for numerical optimization

This paper presents a novel biologically inspired metaheuristic algorithm called seven-spot ladybird optimization (SLO). The SLO is inspired by recent discoveries on the foraging behavior of a seven-spot ladybird. In this paper, the performance of the SLO is compared with that of the genetic algorithm, particle swarm optimization, and artificial bee colony algorithms by using five numerical benchmark functions with multimodality. The results show that SLO has the ability to find the best solution with a comparatively small population size and is suitable for solving optimization problems with lower dimensions.

HACO2 Method for Evolving Hyperbox Classifiers with Ant Colony Optimization

A method, called HACO2 (Hyperbox classifier with Ant Colony Optimization - type 2), is proposed for evolving a hyperbox classifier using the ant colony meta-heuristic. It reshapes the hyperboxes in a near-optimal way to better fit the data, improving the accuracy and possibly indicating its most discriminative features. HACO2 is validated using artificial 2D data showing over 90% accuracy. It is also applied to the benchmark iris data set (4 features), providing results with over 93% accuracy, and to the MIS data set (11 features), with almost 85% accuracy. For these sets, the two most discriminative features obtained from the method are used in simplified classifiers which result in accuracies of 100% for the iris and 83% for the MIS data sets. Further modifications (automatic parameter setting), extensions (initialization short comings) and applications are discussed.