The objective of the p-center problem is to locate p-centers on a network such that the maximum of the distances from each node to its nearest center is minimized. The artificial bee colony algorithm is a swarm-based meta-heuristic algorithm that mimics the foraging behavior of honey bee colonies. This study proposes a modified ABC algorithm that benefits from a variety of search strategies to ...

Swarm intelligence is a research branch that models the population of interacting agents or swarms that are able to self-organize. An ant colony, a flock of birds or an immune system is a typical example of a swarm system. Bees’ swarming around their hive is another example of swarm intelligence. Artificial Bee Colony (ABC) Algorithm is an optimization algorithm based on the intelligent behavio...

The artificial bee colony (ABC) algorithm is a swarm-based metaheuristic optimization technique, developed by inspiring foraging and dance behaviors of honey bee colonies. ABC consists of four phases named as initialization, employed bee, onlooker bee and scout bee. The employed bees try to improve their solution in employed bees phase. If an employed bee cannot improve self-solution in a certa...

An enhanced Artificial Bee Colony (ABC) optimization algorithm, which is called the Interactive Artificial Bee Colony (IABC) optimization, for numerical optimization problems, is proposed in this paper. The onlooker bee is designed to move straightly to the picked coordinate indicated by the employed bee and evaluates the fitness values near it in the original Artificial Bee Colony algorithm in...

UNLABELLED The sequential algorithm for fibrosis evaluation (SAFE) and the Bordeaux algorithm (BA), which cross-check FibroTest with the aspartate aminotransferase-to-platelet ratio index (APRI) or FibroScan, are very accurate but provide only a binary diagnosis of significant fibrosis (SAFE or BA for Metavir F ≥ 2) or cirrhosis (SAFE or BA for F4). Therefore, in clinical practice, physicians h...

In this paper we present a new, non-pheromone-based algorithm inspired by the behaviour of biological bees. The algorithm combines both recruitment and navigation strategies. We investigate whether this new algorithm outperforms pheromone-based algorithms in the task of foraging. From our experiments, we conclude that (i) the non-pheromone-based algorithm is significantly more efficient when fi...

Feature selection plays an important role in data mining and pattern recognition, especially in the case of large scale data. Feature selection is done due to large amount of noise and irrelevant features in the original data set. Hence, the efficiency of learning algorithms will increase incredibly if these irrelevant data are removed by this procedure. A novel approach for feature selection i...

The multiprocessor scheduling is one of the NP-complete scheduling problems. This problem comes when a known parallel program must be executed on a parallel computer. Different methods and algorithms have been tested for this scheduling problem. This paper presents and tests a hybrid bee algorithm. In this approach, the bee algorithm is combined with a heuristic in order to produce quickly good...

In this paper we compare the performance of Minton’s et al. min-conflicts hill-climbing (MCHC) algorithm [16], Wallace and Freuder’s MCHC algorithm [25], Morris’ breakout algorithm (BA) [17, 18], and modified variants of the BA that we developed on static constraint satisfaction problems (CSPs) and recurrent dynamic constraint satisfaction problems (rDCSPs). Also, we used a simple evolutionary ...

Meta-heuristic algorithms are applied in optimization problems in a variety of fields, including engineering, economics, and computer science. In this paper, seven population-based meta-heuristic algorithms are employed for size and geometry optimization of truss structures. These algorithms consist of the Artificial Bee Colony algorithm, Cyclical Parthenogenesis Algorithm, Cuckoo Search algori...