The next generation tactical networks will be based on mobile ad hoc networks (MANETs). These networks require as well a stable clustered network structure as an efficient channel assignment optimization method. Efficient spatial channel reuse provides network scalability and high spectral efficiency. In this paper, a centralized clustering algorithm scheme based on ant colony optimization (ACO...

Ant Colony Optimization (ACO) is a metaheuristic inspired by the foraging behavior of ant colonies that has been successful in the resolution of hard combinatorial optimization problems like the Traveling Salesman Problem (TSP). This paper proposes the Omicron ACO (OA), a novel population-based ACO alternative originally designed as an analytical tool. To experimentally prove OA advantages, thi...

A hybrid Orthogonal Scheme Ant Colony Optimization (OSACO) algorithm for continuous function optimization (CFO) is presented in this paper. The methodology integrates the advantages of Ant Colony Optimization (ACO) and Orthogonal Design Scheme (ODS). OSACO is based on the following principles: a) each independent variable space (IVS) of CFO is dispersed into a number of random and movable nodes...

Ant colony optimization (ACO) and its variants are applied extensively to resolve various continuous optimization problems. As per the various diversification and intensification schemes of ACO for continuous function optimization, researchers generally consider components of multidimensional state space to generate the new search point(s). However, diversifying to a new search space by updatin...

Ant colony optimization (ACO) algorithms are a recently developed, population-based approach which has been successfully applied to combinatorial optimization problems. However, in the ACO algorithms, it is difficult to adjust the balance between intensification and diversification and thus the performance is not always very well. In this paper we proposed an improved ACO algorithm in which a s...

Quality of Service (QoS) anycast routing problem is a nonlinear combination optimization problem, which is proved to be a NP-complete problem, at present, the problem can be prevailingly solved by heuristic methods. Ant colony optimization algorithm (ACO) is a novel random search algorithm. On the one hand, it does not depend on the specific mathematical description, on the other hand, which ha...

In recent year access rich information using multimedia services via the internet from mobile devices. Then social application shares the information using routing algorithm. Routing means the act of moving information across an internet work from a source to a destination. In this paper, Swarm intelligence follows the behaviour of cooperative ants in order to solve hard static and dynamic opti...

In this paper we first provide a thorough performance comparison of the three main Ant Colony Optimization (ACO) paradigms for the Vehicle Routing Problem (VRP), namely the Rank based Ant System, the Max-Min Ant System and the Ant Colony System. Based on the results of this comparison we then implement a parallelization strategy to increase computational efficiency and study the effects of incr...

Ant colony optimization (ACO) algorithm is a new heuristic algorithm which has been demonstrated a successful technology and applied to solving complex optimization problems. But the ACO exists the low solving precision and premature convergence problem, particle swarm optimization (PSO) algorithm is introduced to improve performance of the ACO algorithm. A novel hybrid optimization (HPSACO) al...

Ant Colony Optimization (ACO) is a well-known swarm intelligence method, inspired in the social behavior of ant colonies for solving optimization problems. When facing large and complex problem instances, parallel computing techniques are usually applied to improve the efficiency, allowing ACO algorithms to achieve high quality results in reasonable execution times, even when tackling hard-to-s...