Cluster analysis of acrylates to guide sampling for toxicity testing
Journal of Chemical Information & Computer Sciences
The dynamics of collective sorting robot-like ants and ant-like robots
Proceedings of the first international conference on simulation of adaptive behavior on From animals to animats
Diversity and adaptation in populations of clustering ants
SAB94 Proceedings of the third international conference on Simulation of adaptive behavior : from animals to animats 3: from animals to animats 3
Ant-like agents for load balancing in telecommunications networks
AGENTS '97 Proceedings of the first international conference on Autonomous agents
Swarm intelligence: from natural to artificial systems
Swarm intelligence: from natural to artificial systems
Stigmergy, self-organization, and sorting in collective robotics
Artificial Life
Future Generation Computer Systems
An Ants heuristic for the frequency assignment problem
Future Generation Computer Systems
Swarm intelligence
Improved Ant-Based Clustering and Sorting
PPSN VII Proceedings of the 7th International Conference on Parallel Problem Solving from Nature
Adaptive Task Allocation Inspired by a Model of Division of Labor in Social Insects
Biocomputing and emergent computation: Proceedings of BCEC97
Exact and Approximate Nondeterministic Tree-Search Procedures for the Quadratic Assignment Problem
INFORMS Journal on Computing
HAS-SOP: Hybrid Ant System for the Sequential Ordering Problem
HAS-SOP: Hybrid Ant System for the Sequential Ordering Problem
Ant colony system: a cooperative learning approach to the traveling salesman problem
IEEE Transactions on Evolutionary Computation
Ant system: optimization by a colony of cooperating agents
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Learning and Intelligent Optimization
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A novel adaptive ant colony clustering algorithm based on digraph (A3CD) is presented. Inspired by the swarm intelligence shown through the social insects' self-organizing behavior, in A3CD we assign acceptance weights on the directed edges of a pheromone digraph. The weights of the digraph is adaptively updated by the pheromone left by ants in the seeking process. Finally, strong connected components are extracted as clusters under a certain threshold. A3CD has been implemented and tested on several clustering benchmarks and real datasets to compare the performance with the classical K-means clustering algorithm and LF algorithm which is also based on ACO. Experimental results show that our algorithm is easier to implement, more efficient and performs faster and has better clustering quality than other methods.