Abstract: One of the equipment that can help improve distribution system status today and reduce the cost of fault time is remote control switches (RCS). Finding the optimal location and number of these switches in the distribution system can be modeled with various objective functions as a nonlinear optimization problem to improve system reliability and cost. In this article, a particle swarm ...

Research on optimization in continuous domains gains much of focus in swarm computation recently. A hybrid ant colony optimization approach which combines with the continuous population-based incremental learning and the differential evolution for continuous domains is proposed in this paper. It utilizes the ant population distribution and combines the continuous population-based incremental le...

Ant colony optimization (ACO) algorithms have been applied successfully to combinatorial optimization problems. More recently, Parpinelli et al have applied ACO to data mining classification problems, where they introduced a classification algorithm called Ant Miner. In this paper, we present a system that combines both the proposed Improved Quickreduct algorithm for data preprocessing and ant ...

Ant Colony optimization has proved suitable to solve a wide range of combinatorial optimization (or NP-hard) problems as the Travelling Salesman Problem (TSP). The first step of ACO algorithm is to set the parameters that drive the algorithm. The parameter has an important impact on the performance of the ant colony algorithm. The basic parameters that are used in ACO algorithms are; the relati...

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...