Ant Colony Optimization is a population-based meta-heuristic that exploits a form of past performance memory that is inspired by the foraging behavior of real ants. The behavior of the Ant Colony Optimization algorithm is highly dependent on the values defined for its parameters. Adaptation and parameter control are recurring themes in the field of bio-inspired optimization algorithms. The pres...

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...

The Multi-Depot Vehicle Routing Problem (MDVRP) is a generalization of SDVRP, in which multiple vehicles start from multiple depots and return to their original depots at the end of their assigned tours. The MDVRP is NP-hard, therefore, the development of heuristic algorithms for this problem class is of primary interest. This paper solves Multi-Depot Vehicle Routing Problem with Cellular Ant A...

one of the main problems in the quality control of rivers is the high control cost imposed upon pollutant units. the cooperative policy approach in the treatment process between two or more pollutant units is a new and economic perspective in the environmental management of catchment basins. origins of large amounts pollutants which require a high cost to control pollution are trying to start p...

in this paper, a new robust controller based on geometric homogeneity and adaptive integral sliding mode is proposed for a class of second order systems. the upper bound of the system disturbances is not required. fully unknown parameters have been considered in the described model and its finite–time convergence to zero equilibrium point is proved. moreover, the controller is developed in the ...

During the last years several ant-based techniques were involved to solve hard and complex optimization problems. The current paper is a short study about the influence of artificial ant species in solving optimization problems. There are studied the artificial Pharaoh Ants, Lasius Niger and also artificial ants with no special specificity used commonly in Ant Colony

This paper introduces one of the most profound Swarm Intelligence-based approaches, namely Ant Colony Optimization (ACO). The different operators in ACO and their effect on the quality of solution are briefly discussed. The ACO algorithms variants like Ant System (AS), Elitist Ant System (EAS), Rankbased Ant System (ASrank), Min-Max based Ant System (MMAS) are detailed. The paper puts forth a s...

Web mining has been very crucial to any organization as it provides useful insights to business patterns. It helps the company to understand its customers better. As the web is growing in pace, so is its importance and hence it becomes all the more necessary to find useful patterns. Here in this paper, web mining using ant colony optimization has been reviewed with some of its experimental resu...

With the speeding up of urbanization in our country, the situation of urban emergency is getting more and more serious; therefore, it is necessary to promote the study in urban emergency management, so as to enhance the city ability of resisting the emergency. Based on the defects of the emergency vehicle routing choosing, the paper puts forward a particle swarm optimization based ant colony al...

A two-stage optimization method is presented by employing the evolutionary structural optimization (ESO) and ant colony optimization (ACO), which is called ESO-ACO method. To implement ESO-ACO, size optimization is performed using ESO, first. Then, the outcomes of ESO are employed to enhance ACO. In optimization process, the weight of double layer grid is minimized under various constraints whi...