INTRODUCTION Ant Colony Optimization (ACO) is a relatively new computational intelligence paradigm inspired by the behaviour of natural ants (Dorigo & Stutzle, 2004). Ants often find the shortest path between a food source and the nest of the colony without using visual information. In order to exchange information about which path should be followed, ants communicate with each other by means o...

algorithm (E&D ANTS) to minimize the time delay in transferring a fixed number of data from the source nodes to the destination nodes in an energy-constrained manner. In the algorithm, an amount of artificial ants randomly explored the network and exchanged collected network information to periodically update ant routing-tables which were obtained by having integrated partial pheromones and heu...

In this paper we present a new optimization algorithm based on a model of the foraging behavior of a population of primitive ants (Pachycondyla apicalis). These ants are characterized by a relatively simple but efficient strategy for prey search in which individuals hunt alone and try to cover a given area around their nest. The ant colony search behavior consists of a set of parallel local sea...

Routing algorithm is the key element in networks performance and reliability, therefore, there is a need for an algorithm to manage traffic flows and deliver packets from the source to the destination in a realistic time. A modification algorithm has been presented called Subset Nodes Based AntNet. In the Subset Nodes Based AntNet, a routing tables maintained at each node is used to get a subse...

This paper introduces Vector Ant Colony Optimization (VACO), a distributed algorithm that is applied to solve the traveling salesman problem (TSP). In Any Colony System (ACS), a set of cooperating agents called ants cooperate to find good solutions of TSPs. Ants cooperate using an indirect form of communication mediated by pheromone they deposit on the edges of the TSP graph while building solu...

The Antlion Optimization (ALO) algorithm is a meta-heuristic optimization based on the hunting of ants by antlions. basic inadequacy this that it has long run time because random walk model used for ant's movement. We improved some mechanisms in ALO algorithm, such as ants' walking, reproduction, sliding towards antlion, elitism, and selection procedure. This proposed called Improved (IALO) alg...

Grid computing utilizes the distributed heterogeneous resources in order to support complicated computing problems. Grid can be classified into two types: computing grid and data grid. Job scheduling in computing grid is a very important problem. To utilize grids efficiently, we need a good job scheduling algorithm to assign jobs to resources in grids. In the natural environment, the ants have ...

The Boolean Satisfiability Problem (BSAT) is one of the most important decision problems in mathematical logic and computational sciences for determining whether or not a solution to formula.. Hopfield neural network (HNN) major type artificial (NN) popularly known it used solving various optimization based on its energy minimization machinism. existing models that incorporate standalone projec...

Metaheuristic optimization techniques have been used to solve engineering problems with an increasing speed for the last 30 years. Most of these algorithms developed by imitating a process in nature. In this study, ant colony algorithm inspired natural life ants is discussed. The requires some parameters perform optimization, as other meta-heuristic algorithms. aim study examine effect values o...

Many dynamical networks, such as the ones that produce the collective behavior of social insects, operate without any central control, instead arising from local interactions among individuals. A well-studied example is the formation of recruitment trails in ant colonies, but many ant species do not use pheromone trails. We present a model of the regulation of foraging by harvester ant (Pogonom...