This paper presents improved Ant Colony Optimization (ACO) algorithms for data mining. The goal of the algorithms is to extract classification rules from data. The traditional Ant Colony Optimization algorithm is enhanced with genetic operators to develop improved ACO algorithms. The genetic operators like crossover, mutation are used to develop Ant Colony Optimization with Crossover (ACOC), AC...

In order to solve the low searching efficiency in the peer-to-peer (P2P) network, introduce the ant colony algorithm with the particle swarm optimization in searching procedure. Present a new adaptive search protocol (SACASP) based on the ant colony algorithm with the particle swarm optimization in the Peer-to-Peer Network. The approach simulates the process of the ants’ searching food, and can...

The Multi-Depot Vehicle Routing Problem (MDVRP) is a generalization of SDVRP, in which multiple vehicles start from multiple depots and return to their original depots at the end of their assigned tours. The MDVRP is NP-hard, therefore, the development of heuristic algorithms for this problem class is of primary interest. This paper solves Multi-Depot Vehicle Routing Problem with Cellular Ant A...

clustering is a widespread data analysis and data mining technique in many fields of study such as engineering, medicine, biology and the like. the aim of clustering is to collect data points. in this paper, a cultural algorithm (ca) is presented to optimize partition with n objects into k clusters. the ca is one of the effective methods for searching into the problem space in order to find a n...

Ant miner is a data mining algorithm based on Ant Colony Optimization (ACO). Ant miner algorithms are mainly for discovery rule for optimization. Ant miner + algorithm uses MAX-MIN ant system for discover rules in the database. Soil classification deals with the systematic categorization of soils based on distinguished characteristics as well as criteria. The proposed model delivers to Ant mine...

A distinctive optimization technique known as Ant Colony Optimization (ACO) has gained huge popularity in these recent years due to its flexibility and the ability to avoid reaching local optima. This optimization approach has become a candidate approach for many optimization problems. Unfortunately, this attractive algorithm suffers several downsides including stagnation and slow convergence t...

An Ant Colony Optimization (ACO) algorithm is proposed for optimal tree-structured natural gas distribution network. Design of pipelines, facilities, and equipment systems are necessary tasks to configure an optimal natural gas network. A mixed integer programming model is formulated to minimize the total cost in the network. The aim is to optimize pipe diameter sizes so that the location-alloc...

  The clustering problem under the criterion of minimum sum of squares is a non-convex and non-linear program, which possesses many locally optimal values, resulting that its solution often falls into these trap and therefore cannot converge to global optima solution. In this paper, an efficient hybrid optimization algorithm is developed for solving this problem, called Tabu-KM. It gathers the ...

For the shortcoming that the PI controller parameters can’t been dynamic tuning in constant voltage control system of doubly-fed wind turbines, a PI controller parameters dynamic tuning strategy based on the ant colony optimization (ACO) algorithm is presented. This strategy makes the two parameters in PI controller as the ant of the ant colony, the controlled absolute error integral function t...