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 paper a multi-parameter A*(Astar)-ants based algorithm is proposed in order to find the best optimized multi-parameter path between two desired points in regions. This algorithm recognizes paths, according to user desired parameters using electronic maps. The proposed algorithm is a combination of A* and ants algorithm in which the proposed A* algorithm is the prologue to the suggested ...

The one-dimensional cutting stock problem, has so many applications in lots of industrial processes and during the past few years has attracted so many researchers’ attention all over the world. In this paper a meta-heuristic method based on ACO is presented to solve this problem. In this algorithm, based on designed probabilistic laws, artificial ants do select various cuts and then select the...

The most widely used algorithm for Monte Carlo sampling of electronic transitions in trajectory surface hopping (TSH) calculations is the so-called anteater algorithm, which is inefficient for sampling low-probability nonadiabatic events. We present a new sampling scheme (called the army ants algorithm) for carrying out TSH calculations that is applicable to systems with any strength of couplin...

Purpose. At present, the computer networks of information and telecommunication system (ITS) railway transport use OSPF protocol, which does not allow taking into account several metrics when determining optimal route. Therefore, there is a need to study possibility organizing routing in rail ITS using two-colonial ant algorithm. Methodology. According Two-ACO software model, created Python lan...

In this paper we will present a new clustering algorithm for unsupervised learning. It is inspired from the self-assembling behavior observed in real ants where ants progressively become attached to an existing support and then successively to other attached ants. The artificial ants that we have defined will similarly build a tree. Each ant represents one data. The way ants move and build this...

Genome rearrangement is an important area in computational biology and bioinformatics. The basic problem in genome rearrangements is to compute the edit distance, i.e., the minimum number of operations needed to transform one genome into another. Unfortunately, unsigned genome rearrangement problem is NP-hard. In this study an improved ant colony optimization algorithm to approximate the edit d...

This paper studies the characteristics and behavior of AntNet routing algorithm and introduces two complementary strategies to improve its adaptability and robustness particularly under unpredicted traffic conditions such as network failure or sudden burst of network traffic. Both of the proposed strategies use the knowledge of backward ants with undesirable trip times called Dead Ants to balan...

Ant colony optimization (ACO) algorithms are population-based algorithms where ants communicate via their pheromone trails. Usually, this indirect communication leads the algorithm to a stagnation behaviour, where the ants follow the same path from early stages. This is because high levels of pheromone are generated into a single trail, where all the ants are influenced and follow it. As a resu...

the one-dimensional cutting stock problem, has so many applications in lots of industrial processes and during the past few years has attracted so many researchers’ attention all over the world. in this paper a meta-heuristic method based on aco is presented to solve this problem. in this algorithm, based on designed probabilistic laws, artificial ants do select various cuts and then select the...