An improved artificial bee colony algorithm was proposed for data clustering aiming at overcoming the shortcomings of being trapped in local optimum and slow convergence rate in this paper
called artificial colonyclustering algorithm based on global information (GI-ABC). On the one hand
the modified algorithm added the average richness of food to the updating equation of food source
taking advantage of the intermediate clustering consequence to update the food source better. On the other hand
it utilized global information to improve the search efficiency of the onlooker bees
which contributed to getting the global optimum. Meanwhile
abundant experiments were conducted to evaluate it. In the experiments
four of topical real data sets selected from the UCI Machine Learning Repository were used to test the performance of the strategies compared with the other clustering algorithms
such as ABC algorithm
Particle Swarm Optimization (PSO) algorithm and K-means algorithm. The results indicate that the modified algorithm can generate better results than other algorithms and has better general performance.
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