In this paper multiobjective optimization problem simultaneously optimized using hybrid PS-ACO algorithm has been attempted. The methodology used in this paper is based upon the information sharing and movement of swarms or particles in a search space, and further applying ACO on the result obtained by the PSO. Multiobjective optimization problems are present at physical design level at partiti...

Aconitine (ACO), a highly toxic diterpenoid alkaloid, is recognized to have effects on cardiac voltage-gated Na(+) channels. However, it remains unknown whether it has any effects on K(+) currents. The effects of ACO on ion currents in differentiated clonal cardiac (H9c2) cells and in cultured neonatal rat ventricular myocytes were investigated in this study. In H9c2 cells, ACO suppressed ultra...

Standard pattern classifiers perform on all data features. Whereas, some of the features are redundant or irrelevant, which reduce prediction accuracy, and increase running time of classifier. The purpose of this study is to search optimal feature subset, in order to increase the classification performance. The feature selection problem differs from traditional optimization problem on the probl...

Problem statement: Efficient scheduling of the tasks to heterogeneous processors for any application is critical in order to achieve high performance. Finding a feasible schedule for a given task set to a set of heterogeneous processors without exceeding the capacity of the processors, in general, is NP-Hard. Even if there are many conventional approaches available, people have been looking at ...

This paper presented a new ant colony optimization (ACO) feature selection method to classify hand motion surface electromyography (sEMG) signals. The multiple channels of sEMG recordings make the dimensionality of sEMG feature grow dramatically. It is known that the informative feature subset with small size is a precondition for the accurate and computationally efficient classification strate...

As it is commonly understood, Urban neighborhoods play a significant role in developing urbansustainability as the smallest unit of city so that the formation and promotion of neighborhood identity, and focusingon social relationships would be in harmony with urban sustainability. It is believed that a dramatic change in spatialstructure of urban neighborhood include a decline in levels of poli...

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...

Norton Healthcare, an integrated delivery system based in Louisville, Ky., is one of the provider groups taking part in the Brookings–Dartmouth ACO Pilot Program to form accountable care organizations, which assume responsibility for improving patient care and lowering total costs and, in turn, share in the savings achieved. This case study explores the characteristics of Norton and its partner...

We propose a new algorithm based on the Ant Colony Optimization (ACO) meta-heuristic for the Multidimensional Knapsack Problem, the goal of which is to find a subset of objects that maximizes a given objective function while satisfying some resource constraints. We show that our new algorithm obtains better results than two other ACO algorithms on most instances.

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...