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This paper discusses a new adaptive ant colony optimization algorithm and its characteristics are as follows: (1) a novel cranky ant who behaves strangely is introduced to prevent from trapping at the local optima, (2) a new observation technique for searching status is adopted to judge whether it is trapping at local optima. Experimental results using benchmark data prove that the proposed algorithm with the cranky ants and the observation technique enables to control the trade-off between intensification and diversification, in comparison with conventional ACO. [ABSTRACT FROM AUTHOR]

In this paper a new method based on the Ant-Miner algorithm is proposed to discover sets of unstructured classification rules. This method, called the Tree-Miner, creates a directed graph made up of nodes representing operators and operands. Each ant in a colony of artificial ants traverses this graph to find routes that represent the best unstructured rule antecedents. These antecedents are used to classify the given data and are also interpreted as knowledge hidden in the training data. The performance of the Tree-Miner algorithm was evaluated against that of the Ant-Miner according to the accuracy and the simplicity of the constructed rules. The results showed that our method has an acceptable predictive accuracy while discovering rules that are simpler and more comprehensive. [ABSTRACT FROM AUTHOR]

A hybrid population based Ant Colony Optimization (ACO) algorithm PFold-P-ACO for protein folding in the HP model is proposed in this paper. This is the first population based ACO algorithm in the bioinformatics. It is shown experimentally that the algorithms achieves on nearly all test sequences at least comparable results to other state of the art algorithms. Compared to the state of the art ACO algorithm PFold-P-ACO slightly better results and is faster on long sequences. [ABSTRACT FROM AUTHOR]

A comparison of six basic Ant Colony Optimization (ACO) in dynamic environment was studied in this paper. Dynamic Traveling Salesman Problem (TSP) will be used as a dynamic environment. A number of cities are swap over time to make the TSP environment dynamic. A pheromone equalization strategy was applied in all the six ACO to react to the change. Three sets of TSP are used in this experiment. The result will show which of the six basic ant algorithms work best in dynamic environment. [ABSTRACT FROM AUTHOR]