In allusion to the deficiencies of the ant colony optimization algorithm for solving the complex problem, the genetic algorithm is introduced into the ant colony optimization algorithm in order to propose a novel hybrid optimization (NHGACO) algorithm in this paper. In the NHGACO algorithm, the genetic algorithm is used to update the global optimal solution and the ant colony optimization algor...

Ant colony optimization (ACO) has proved its success as a meta-heuristic optimization in several network applications such as routing and load balancing. In this paper, a proposed ACO algorithm for load balancing in distributed systems will be presented. This algorithm is fully distributed in which information is dynamically updated at each ant movement. Multiple colonies paradigm will be adopt...

Ant Colony Optimization (ACO) [2] is a nondeterministic algorithm framework that mimics the foraging behavior of ants to solve difficult optimization problems. Several researchers have successfully applied ant based algorithm framework in different fields of engineering, but never in VLSI testing. In this paper, we first describe the basics of ACO. We then consider the problem of simultaneously...

-The ant colony optimization takes insipiration from the foraging behavior of some ant species.These ants deposits pheromone on the ground in order to make some suitable path that should be followed by other members of the colony.The goal of this paper is to introduce ant colony optimization and to survey its most notable applications.In this paper we focus on some research efforts directed at ...

The purpose of this article is to review the literature on the topic of deterministic vehicle routing problem (VRP) and to give a review on the exact and approximate solution techniques. More specifically the approximate (meta-heuristic) solution techniques are classified into: tabu search, simulated annealing, genetic algorithm, evolutionary algorithm, hybrid algorithm, and Ant Colony Opti...

A method of automatic programming, called genetic programming, assumes that the desired program is found by using a genetic algorithm. We propose an idea of ant colony programming in which instead of a genetic algorithm an ant colony algorithm is applied to search for the program. The test results demonstrate that the proposed idea can be used with success to solve the approximation problems.

the traveling salesman problem (tsp) is a well-known combinatorial optimization problem and holds a central place in logistics management. the tsp has received much attention because of its practical applications in industrial problems. many exact, heuristic and metaheuristic approaches have been proposed to solve tsp in recent years. in this paper, a modified ant colony optimization (maco) is ...