Ant colony optimization (ACO) algorithms are a recently developed, population-based approach which has been successfully applied to optimization problems. However, in the ACO algorithms it is difficult to adjust the balance between intensification and diversification and thus the performance is not always very well. In this work, we propose an improved ACO algorithm in which some of ants can ev...

Decision trees have been widely used in data mining andmachine learning as a comprehensible knowledge representation. While ant colony optimization (ACO) algorithms have been successfully applied to extract classification rules, decision tree induction with ACO algorithms remains an almost unexplored research area. In this paper we propose a novel ACO algorithm to induce decision trees, combini...

Survey-ACO in Task Scheduling problem Vinothina V Department of Computer Science, Garden City College, Bangalore-39 Email: [email protected] Dr.R.Sridaran Faculty of Computer Applications, Marwadi Education Foundation’s Group of Institutions, Rajkot, Gujarat, India. Email: [email protected] ----------------------------------------------------------------------ABSTRACT-----------------...

Ant Colony Optimization (ACO) has been successfully applied to solve a wide range of combinatorial optimization problems such as minimum spanning tree, traveling salesman problem, and quadratic assignment problem. Basic ACO has drawbacks of trapping into local minimum and low convergence rate. Simulated annealing (SA) and mutation operator have the jumping ability and global convergence; and lo...

The computational complexity of ant colony optimization (ACO) is a new and rapidly growing research area. The finite-time dynamics of ACO algorithms is assessed with mathematical rigor using bounds on the (expected) time until an ACO algorithm finds a global optimum. We review previous results in this area and introduce the reader into common analysis methods. These techniques are then applied ...

Bayesian networks are a useful tool in the representation of uncertain knowledge. This paper proposes a new algorithm to learn the structure of a Bayesian network. It does this by conducting a search through the space of equivalence classes of Bayesian networks using Ant Colony Optimization (ACO). To this end, two novel extensions of traditional ACO techniques are proposed and implemented. Firs...

The Ant Colony Optimization (ACO) algorithms are being applied successfully to a wide range of problems. ACO algorithms could be good alternatives to existing algorithms for hard combinatorial optimization problems (COPs). In this paper we investigate the influence of the probabilistic model in model-based search as ACO. We present the effect of four different probabilistic models for ACO algor...

Ant Colony Optimization (ACO) is applied to two dynamic job scheduling problems, which have the same mean total workload but different dynamic levels and disturbing severity. Its performances are statistically analysed and the effects of its adaptation mechanism and parameters such as the minimal number of iterations and the size of searching ants are studied. The results show that ACO can perf...

the edges of an image define the image boundary. when the image is noisy, it does not become easy to identify the edges. therefore, a method requests to be developed that can identify edges clearly in a noisy image. many methods have been proposed earlier using filters, transforms and wavelets with ant colony optimization (aco) that detect edges. we here used aco for edge detection of noisy ima...

Sudden cardiac death in a young patient is a catastrophic occurrence. Anomalous coronary origin (ACO) is a significant cause of sudden cardiac death among individuals under the age of 35 years. We sought to define the premortem clinical and postmortem histopathologic findings in victims of sudden cardiac death resulting from either ACO or obstructive atherosclerotic coronary artery disease (CAD...