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2006•Microelectronics & ComputerRequires access

A New Clustering Algorithm Based on Hybrid Ant Colony Algorithm

Jingyu Yang

Open publisher page 1 citations

Abstract

An optimization model of clustering problem is given in this paper.The advantages and shortages of K-Means algorithm,simulated annealing algorithm and basic ant colony algorithm are analyzed.The algorithm is then extended to use K-means clustering to seed the initial solution and the information pheromone is adjusted according to them.All the 2 hybrid ant colony algorithms are proved effective and especially the second hybrid algorithm is a best algorithm than others.

About this research paper

What this paper is about

An optimization model of clustering problem is given in this paper.The advantages and shortages of K-Means algorithm,simulated annealing algorithm and basic ant colony algorithm are analyzed.The algorithm is then extended to use K-means clustering to seed the initial solution and the information pheromone is adjusted according to them.All the 2 hybrid ant colony algorithms are proved effective and especially the second hybrid algorithm is a best algorithm than others.

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OpenAlex reports 1 citations for this work. Citation counts describe recorded attention and do not establish research quality.

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Available abstract

An optimization model of clustering problem is given in this paper.The advantages and shortages of K-Means algorithm,simulated annealing algorithm and basic ant colony algorithm are analyzed.The algorithm is then extended to use K-means clustering to seed the initial solution and the information pheromone is adjusted according to them.All the 2 hybrid ant colony algorithms are proved effective and especially the second hybrid algorithm is a best algorithm than others.

Key concepts: Computer science, Ant colony optimization algorithms, Algorithm, Cluster analysis, Simulated annealing, Economic shortage, Hybrid algorithm (constraint satisfaction), k-medoids

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