Ant Colony Optimization (ACO) is a promising new approach to combinatorial optimization. Here ACO is applied to the traveling salesman problem (TSP). Using a genetic algorithm (GA) to nd the best set of parameters, we demonstrate the good performance of ACO in nding good solutions

In this paper is introduce "flying" ants in Ant Colony Optimization (ACO). In traditional ACO algorithms the ants construct their solution regarding one step forward. In proposed ACO algorithm, the ants make their decision, regarding more than one step forward, but they include only one new element in their solutions.

Load balancing of virtual machines is one of the most significant issues in cloud computing research. A common approach is to employ intelligent algorithms such as Ant Colony Optimization (ACO). However, there are two main issues with traditional ACO. First, ACO is very dependent on the initial conditions, which might affect the final optimal solution and the convergence speed. To solve this pr...

The phytohormone ethylene is essential for senescence in many fruits and flowers. In the biosynthesis of ethylene in plants, the conversion of S-adenosyl methionine (SAM) to 1-aminocyclopropane1-carboxylic acid (ACC) and the conversion of ACC to ethylene are catalyzed by ACC synthase (ACS) and ACC oxidase (ACO), respectively (Adams and Yang, 1979). Using a cDNA clone representing carnation ACO ...

Lung cancer is becoming a threat to mankind. Applying machine learning algorithms for detection and segmentation of irregular shaped lung nodules remains a remarkable milestone in CT scan image analysis research. In this paper, we apply ACO algorithm for lung nodule detection. We have compared the performance against three other algorithms, namely, Otsu algorithm, watershed algorithm, and globa...

In this study, we present a hybrid approach of Ant Colony Optimization algorithm (ACO) with fuzzy logic and clustering methods to solve multiobjective path planning problems in the case swarm Unmanned Surface Vehicles (USVs). This study aims further explore performance ACO by integrating order cope multiple contradicting objectives generate quality solutions in-parallel identifying mission area...

INTRODUCTION Cell count in induced sputum is a noninvasive biomarker to assess airway inflammation phenotypes. Accordingly, sputum cell counts are extensively used in the treatment of asthma and COPD. Nevertheless, the clinical application of sputum cell counts in patients with asthma-COPD overlap (ACO) remains elusive. The aim of this study was to investigate sputum cell counts in patients wit...

Ant colony optimization (ACO) has been proved to be one of the best performing algorithms for NP-hard problems as TSP. The volatility rate of pheromone trail is one of the main parameters in ACO algorithms. It is usually set experimentally in the literatures for the application of ACO. The present paper proposes an adaptive strategy for the volatility rate of pheromone trail according to the qu...

We describe an Ant Colony Optimization (ACO) algorithm, ANT-MPE, for the most probable explanation problem in Bayesian network inference. After tuning its parameters settings, we compare ANTMPE with four other sampling and local search-based approximate algorithms: Gibbs Sampling, Forward Sampling, Multistart Hillclimbing, and Tabu Search. Experimental results on both artificial and real networ...

Ant colony optimization (ACO) is a metaheuristic that produces good results for a wide range of combinatorial optimization problems. Often such successful applications use a combination of ACO and local search procedures that improve the solutions constructed by the ants. In this paper, we study this combination from a theoretical point of view and point out situations where introducing local s...