Ants often form mutualistic interactions with aphids, soliciting honeydew in return for protective services. Under certain circumstances, however, ants will prey upon aphids. In addition, in the presence of ants aphids may increase the quantity or quality of honeydew produced, which is costly. Through these mechanisms, ant attendance can reduce aphid colony growth rates. However, it is unknown ...

The first ant colony optimization (ACO) called ant system was inspired through studying of the behavior of ants in 1991 by Macro Dorigo and co-workers [1]. An ant colony is highly organized, in which one interacting with others through pheromone in perfect harmony. Optimization problems can be solved through simulating ant’s behaviors. Since the first ant system algorithm was proposed, there is...

Angiotensin-converting enzyme inhibitors (ACEi) improve cardiac function and remodeling and prolong survival in patients with heart failure (HF). Blockade of the renin-angiotensin system (RAS) with an angiotensin II type 1 receptor antagonist (AT1-ant) may have a similar beneficial effect. In addition to inhibition of the RAS, ACEi may also act by inhibiting kinin destruction, whereas AT1-ant m...

BACKGROUND Deep brain stimulation (DBS) is a minimally invasive and reversible method to treat an increasing number of neurological and psychiatric disorders, including epilepsy. Targeting poorly defined deep structures is based in large degree on stereotactic atlas information, which may be a major source of inconsistent treatment effects. AIM OF THE STUDY In the present study, we aimed to s...

This article describes Ant Systems a meta heuristic based on an ant foraging metaphor The presentation of Ant Systems has been somewhat generalized by adding a Queen process in charge of co ordinating classi cal Ant processes so that recent Ant Systems can be naturally included while remaining close to the metaphor To illustrate how Ant Systems are practically implemented a number of applicatio...

Set covering problem is the model for many important industrial applications. In this paper, we solve some benchmarks of this problem with ant colony optimization algorithms using a new transition rule. A look-ahead mechanism was incorporated to check constraint consistency in ant computing. Computational results are presented showing the advantages to use this additional mechanism to ant syste...

In view of shortcomings of BP neural network, which is slow to converge and tends to trap in local optimum when applied in fault diagnosis, an approach for fault diagnosis based on BP neural network optimized by chaos ant colony algorithm is proposed. Mathematical model of chaos ant colony algorithm is created. Real-coded method is adopted and the weights and thresholds of BP neural network are...

Only 0.7% of 28,205 known New England ant specimens (1861–2011) were from Rhode Island. Consequently, apparent ant species richness of Rhode Island counties was lower than expected based on simple biogeographic models. Collections from two poorly sampled areas—Block Island and Tiverton—and from the 2013 Rhode Island Natural History Survey’s BioBlitz increased Rhode Island’s ant specimens by 46%...

Two ant algorithms solving a simplified version of a typical university course timetabling problem are presented – Ant Colony System and MAX -MIN Ant System. The algorithms are tested over a set of instances from three classes of the problem. Results are compared with recent results obtained with several metaheuristics using the same local search routine (or neighborhood definition), and a refe...

Optimization of reservoir parameters is an important issue in petroleum exploration and production. The Ant Colony Optimization(ACO) is a recent approach to solve discrete and continuous optimization problems. In this paper, the Ant Colony Optimization is usedas an intelligent tool to estimate reservoir rock properties. The methodology is illustrated by using a case study on shear wave velocity...