Improved Ant Colony Clustering Algorithm and Its Performance Study

Abstract

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.

Hybrid Nearest Neighbors Ant Colony Optimization for Clustering Social Media Comments

“…After that, the artificial ants will pick the object which is dissimilar to its neighborhood, move it to another location with similar objects, and then drop it. The studies [15], [18]- [20] also used this basic concept in their proposed solution.…”

“…That was proven in some studies where the cluster number should be analyzed visually after the clustering was done [19]. Besides that, the iteration number could reach 15000 for clustering Iris dataset [15]. Another ant algorithm for clustering is ACO based clustering which uses the same concept of solution string to represent the clustering solution as explained in section 1.1.2.…”

New Data Clustering Algorithm Combined of Ant Colony Algorithm and Improved Fuzzy C-Means Algorithm

“…A lot of experiments and analysis show that ants will release a substance called pheromone when pass a path and transfer information through the material with the other individual, then the follow ants can identify the presence and degree of the pheromone to determine their marches direction [8,9]. Ant colony algorithm is based on the transmission of information by ants to find the optimal path information of positive feedback phenomenon to find the optimal solution.…”

Ant Colony Optimization With Double Selections for Solving Integrated Scheduling Problem in Manufacturer

“…The other studies implementing ant colony optimization are ant colony optimization for permutation flowshop scheduling [3], ant colony optimization for job shop scheduling [4], applying ant colony optimization to increase the speed of the road network [5], ant colony optimization for solving the shortest path problem [6], ant colony optimization for generalized TSP problem [7], ant colony optimization for solving 3D TSP [8], and implementation of traveling salesman problem using ant colony optimization [9]. Meanwhile, the studies which improving ant colony optimization are solving traveling salesman problem based on ants with memory [10], modified ant colony optimization for grid scheduling [11], an improved ant colony optimization for machines scheduling [12], modified ant colony optimization by applying a local search procedure [13], Development of ant colony optimization based on statistical analysis and hypothesis testing [14], ant colony optimization with fault tolerance [15], a novel hybrid ant colony optimization [16], improved ant colony optimization forweapon-target assignment [17], ant colony clustering algorithm [18], improving ant colony optimization algorithm in transmission status rule [19].…”