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...

<span lang="EN-US">This paper focuses on the optimal sizing of a positive second-generation current conveyor (CCII+), employing hybrid algorithm named DE-ACO, which is derived from combination differential evolution (DE) and ant colony optimization (ACO) algorithms. The basic idea this hybridization to apply DE for ACO algorithm’s initialization stage. Benchmark test functions were used e...

Financial marketing is a method of risky investment, but in shorter duration, the individual can expect profit or loss, depending on current market rate for organization. Though it has higher risk getting profit, many individuals are ready to take chance benefit. In era, much research going provide better prediction rate. Most researchers prefer Ant Colony Optimization (ACO) as more trustworthy...

Ant colony optimisation (ACO) is a metaheuristic to solve optimisation problems inspired by the foraging behaviour of ant colonies. It has been successfully applied to several types of optimisation problems, such as scheduling and routing, and more recently for the discovery of classification rules. The classification task in data mining aims at predicting the value of a given goal attribute fo...

In this paper we present a study of several Ant Colony Optimization (ACO) algorithms for the Set Covering Problem. In our computational study we emphasize the influence of different ways of defining the heuristic information on the performance of the ACO algorithms. Finally, we show that the best performing ACO algorithms we implemented, when combined with a fine-tuned local search procedure, r...

In this article, we propose UACOR, a unified ant colony optimization (ACO) algorithm for continuous optimization. UACOR includes algorithmic components from ACOR; DACOR and IACOR-LS, three ACO algorithms for continuous optimization that have been proposed previously. Thus, it can be used to instantiate each of these three earlier algorithms; in addition, from UACOR we can also generate new cont...

Many real-world optimization problems are subject to dynamic environments that require an optimization algorithm to track the optimum during changes. Ant colony optimization (ACO) algorithms have proved to be powerful methods to address combinatorial dynamic optimization problems (DOPs), once they are enhanced properly. The integration of ACO algorithms with immigrants schemes showed promising ...

BACKGROUND End-tidal carbon dioxide (P(ETCO(2))) is a surrogate, noninvasive measurement of arterial carbon dioxide (P(aCO(2))), but the clinical applicability of P(ETCO(2)) in the intensive care unit remains unclear. Available research on the relationship between P(ETCO(2)) and P(aCO(2)) has not taken a detailed assessment of physiologic dead space into consideration. We hypothesized that P(ET...

AIMS Acute coronary artery occlusion (ACO) occurs in ∼30% of patients with non-ST-segment elevation-acute coronary syndrome (NSTE-ACS). We investigated the ability of a regional non-invasive myocardial work index (MWI) to identify ACO. METHODS AND RESULTS Segmental strain analysis was performed before coronary angiography in 126 patients with NSTE-ACS. Left ventricular (LV) pressure was estim...

The analog circuit design is a hard complex and time consuming task. Recently, Ant Colony Optimization algorithm (ACO) has emerged as a powerful and useful method for optimal design of analog circuits, based on the finding of the good compromise between the most important performances or merits figure of a given circuit. As a drawback it needs longer time execution when compared for instance to...