This paper presents an ant colony optimization methodology for optimally clustering N objects into K clusters. The algorithm employs distributed agents which. AbstractAnt-based clustering is a biologically inspired data. Multi-ant colonies approach for clustering data that consists of some parallel.based on ant colony to solve the unsupervised clustering. Index TermsAnt colony optimization, Clu...

Investment decision making is one of the key issues in financial management. Selecting the appropriate tools and techniques that can make optimal portfolio is one of the main objectives of the investment world. This study tries to optimize the decision making in stock selection or the optimization of the portfolio by means of the artificial colony of honey bee algorithm. To determine the effect...

Using ant colony algorithm to solve TSP (traveling salesman problem) has some disadvantages as easily plunging into local minimum, slow convergence speed and so on. In order to find the optimal path accurately and rapidly, an improved ant colony algorithm is proposed. Experimental results show that the improved ant colony algorithm has better effectiveness for TSP problems solutions.

In multi colony ant algorithms several colonies of ants cooperate in finding good solutions for an optimization problem. At certain time steps the colonies exchange information about good solutions. If the amount of exchanged information is not too large multi colony ant algorithms can be easily parallelized in a natural way by placing the colonies on different processors. In this paper we stud...

By use of the properties of ant colony algorithm and genetic algorithm, a novel ant colony genetic hybrid algorithm, whose framework of hybrid algorithm is genetic algorithm, is proposed to solve the traveling salesman problems. The selection operator is an artificial version of natural selection, and chromosomes with better length of tour have higher probabilities of being selected in the next...