This paper overviews origin and some applications of ant system of optimization. Ants live in colonies. The colony can be regarded as a multiagent organization where each agent performs independently as governed by goal of colony survival.. On observing the behavior of ants, Ant-Algorithm was proposed. The ant algorithms have been extensively studied by European researchers. These algorithms ha...

The clustering algorithms have evolved over the last decade. With the continuous success of natural inspired algorithms in solving many engineering problems, it is imperative to scrutinize the success of these methods applied to data clustering. These naturally inspired algorithms are mainly stochastic search and optimization techniques, guided by the principles of collective behavior and self-...

Ants have always fascinated human beings. What particularly strikes the occasional observer as well as the scientist is the high degree of societal organization that these insects can achieve in spite of very limited individual capabilities (Dorigo et al., 2000). Ants have inspired also a number of optimization algorithms. These algorithms are increasingly successful among researches in compute...

Since the introduction of Ant Colony Optimization (ACO) technique in 1992, the algorithm starts to gain popularity due to its attractive features. However, several shortcomings such as slow convergence and stagnation motivate many researchers to stop further implementation of ACO. Therefore, in order to overcome these drawbacks, ACO is proposed to be combined with Differential Evolution (DE) an...

This paper explores the task scheduling algorithm for cloud computing on the basis of Particle Swarm Optimization (PSO). Based on task scheduling problems of the cloud computing, first of all, this paper detailed introduction to cloud computing, task scheduling of cloud computing, particle swarm optimization algorithm and ant colony optimization algorithm. On this basis of the above, task sched...

Ant colony optimization(ACO), which is one of the intelligential optimization algorithm, has been widely used to solve combinational optimization problems. Deceptive problems have been considered difficult for ant colony optimization. It was believed that ACO will fail to converge to global optima of deceptive problems. This paper proves that the first order deceptive problem of ant colony algo...

The project of ant colony algorithm optimization neural network combining blind equalization algorithm is proposed. The better initial weights of neural networks are provided because of the randomness, ergodicity and positive feedback of the ant colony algorithm. And then, a combination of optimal weights are found through BP algorithm, which is fast local search speed. Thus blind equalization ...

Leaf venation is an important source of data for research in comparative plant biology. This paper presents a method for evolving classifiers capable of extracting the venation from leaf images. Quantitative and qualitative analysis of the classifier produced is carried out. The results show that the method is capable of the extraction of near complete primary and secondary venations with relat...