Healthcare professionals usually should make complex decisions with far reaching consequences and associated risks in health care fields. As it was demonstrated in other industries, the ability to drill down into pertinent data to explore knowledge behind the data can greatly facilitate superior, informed decisions to ensue the facts. Nature has always inspired researchers to develop models of ...

This paper aims at optimizing the parameters involved in stress analysis of perforated plates, in order to achieve the least amount of stress around the square-shaped holes located in a finite isotropic plate using metaheuristic optimization algorithms. Metaheuristics may be classified into three main classes: evolutionary, physics-based, and swarm intelligence algorithms. This research uses Ge...

This paper proposes a novel application of Grammatical Bee Colony for classification of medical data. Grammatical Bee Colony is a Swarm Programming algorithm generally used for automatic computer program generation in any arbitrary language. In this paper, Grammatical Bee Colony based classifier is designed and applied in medical data mining. The proposed method is applied on ten medical data s...

In this paper, a novel hybrid optimization algorithm, which combines a firefly algorithms and Artificial Bee Colony algorithm (FFAABC), is proposed for solving the economic power dispatch (EPD) problem. The hybrid algorithm is structured with two stages. The first stage uses the search by firefly algorithm (FFA) and second stage is a search with Artificial Bee Colony algorithm (ABC). The hybrid...

This paper presents an artificial bee colony (ABC) algorithm adjusted for the capacitated vehicle routing problem. The vehicle routing problem is an NP-hard problem and capacitated vehicle routing problem variant (CVRP) is considered here. The artificial bee metaheuristic was successfully used mostly on continuous unconstrained and constrained problems. Here this algorithm has been implemented ...

The performance of Neural Networks (NN) depends on network structure, activation function and suitable weight values. For finding optimal weight values, freshly, computer scientists show the interest in the study of social insect’s behavior learning algorithms. Chief among these are, Ant Colony Optimzation (ACO), Artificial Bee Colony (ABC) algorithm, Hybrid Ant Bee Colony (HABC) algorithm and ...

A recently invented foraging behavior optimization algorithm which is the Artificial Bee Colony (ABC) algorithm has been widely implemented in addressing various types of optimization problems such as job shop scheduling, constraint optimization problems, complex numerical optimization problems, and mathematical function problems. However, the high exploration ability of conventional ABC has ca...

The set covering problem is a formal model for many practical optimization problems. In the set covering problem the goal is to choose a subset of the columns of minimal cost that covers every row. Here, we present a novel application of the artificial bee colony algorithm to solve the non-unicost set covering problem. The artificial bee colony algorithm is a recent swarm metaheuristic techniqu...

The Weighted Set Covering Problem is a formal model for many practical optimization problems. In this problem the goal is to choose a subset of columns of minimal cost covering every row. Here, we present a novel application of the Artificial Bee Colony algorithm to solve the Weighted Set Covering Problem. The Artificial Bee Colony algorithm is a recent Swarm Metaheuristic technique based on th...

This study adapted an improved algorithm based on Artifical Bee Colony Optimization. It is not possible to justify that all the rules generated by fuzzy based apriori algorithm produce optimum result. Thus optimization of the result generated was carried out by Fuzzy Apriori algorithm using Fuzzy Artifical Bee Colony Optimization (FABCO), it's worth noting that a significant findings were revea...