This paper presents an application of the ant colony metaphor for continuous space optimization problems. The ant algortihm proposed works following the principle of the ant colony approach, i.e., a population of agents iteratively, cooperatively, and independently search for a solution. Each ant in the distributed algorithm applies a local search operator which explores the neighborhood region...

In this paper we deal with the Nurse Scheduling Problem (NSP) in a hospital. The problem aims to generate a feasible and a satisfactory schedule to the planners and the hospital staffs. Sometimes a large number of constraints are taken into account: preferences of staffs (vacation, public holidays, and special demands) and administrative or union obligations, in order to ensure a good working c...

Ant colony optimization algorithms have long been touted as providing an effective and efficient means of generating high quality solutions to NP-hard optimization problems. Unfortunately, while the structure of the algorithm is easy to parallelize, the nature and amount of communication required for parallel execution has meant that parallel implementations developed suffer from decreased solu...

In this paper, 2D seismic data and petrophysical logs of the Pabdeh Formation from four wells of the Mansuri oil field are utilized. ΔLog R method was used to generate a continuous TOC log from petrophysical data. The calculated TOC values by ΔLog R method, used for a multi-attribute seismic analysis. In this study, seismic inversion was performed based on neural networks algorithm and the resu...

Many real world problems can be modelled as an optimization problem. Evolutionary algorithms are used to solve these problems. Ant colony algorithm is a class of evolutionary algorithms that have been inspired of some specific ants looking for food in the nature. These ants leave trail pheromone on the ground to mark good ways that can be followed by other members of the group. Ant colony optim...

In this work, the parallelization of some Multi-Objective Ant Colony Optimization (MOACO) algorithms has been performed. The aim is to get a better performance, not only in running time (usually the main objective when a distributed approach is implemented), but also improving the spread of solutions over the Pareto front (the ideal set of solutions). In order to do this, colony-level (coarsegr...

This paper presents an efficient algorithm for the loss minimization by automatic sectionalizing switch operation in distribution systems. Ant colony algorithm is a multi-agent system in which the behaviour of each single agent, called artificial ant, is inspired by the behaviour of real ants. Ant colony algorithm is suitable for combinatorial optimization. The proposed methodology with some mo...

In this paper we proposed a method of optimal selection of cluster for cluster oriented classifier. The cluster oriented classifier is great advantage over binary and conventional classifier. The cluster oriented classifier work very efficiently on real and sample data. But the cluster oriented ensemble classifier faced a problem of selection of number of cluster for ensemble. In current fashio...

This paper introduces the wireless sensor network routing protocol and its security, and introduces the main several kinds of routing protocols, including the data centric routing protocol, hierarchical routing protocols and location-based routing protocol, finally according to the analysis of routing protocol common attack method. Ant colony algorithm is a distributed parallel algorithm. A sin...

To solve many-objective routing problems, this paper generalizes the Multi-Objective Ant Colony System (MOACS) algorithm, a well-known Multi-Objective Ant Colony Optimization (MOACO) metaheuristic proposed in 2003. This Generalized MOACS algorithm is used to solve a Split-Delivery/Mixed-Fleet Vehicle Routing Problem (SD/MFVRP) under different constraints, resulting from the mathematical modelli...