Artificial Bee Colony (ABC) algorithm is a Nature Inspired Algorithm (NIA) which based in intelligent food foraging behaviour of honey bee swarm. ABC outperformed over other NIAs and other local search heuristics when tested for benchmark functions as well as factual world problems but occasionally it shows premature convergence and stagnation due to lack of balance between exploration and expl...

Artificial bee colony (ABC) algorithm is a swarm intelligence optimization algorithm inspired by the intelligent behavior of honey bees when searching for food sources. Various versions of the ABC algorithm have been widely used to solve continuous and discrete optimization problems in different fields. In this paper, a new binary version of the ABC algorithm inspired by quantum computing calle...

With more and more various systems in nature and society are proved to be modeled as complex networks, community detection in complex networks as a fundamental problem becomes a hot research topic in a large scale of subjects. Artificial Bee Colony Algorithm (ABC) has high efficiency and does not require any prior knowledge about the number or the original division of the communities. So it is ...

Artificial Bee Colony (ABC) algorithm is an optimization algorithm based on the intelligent behavior of honey bee swarm. The ABC algorithm was developed to solve optimizing numerical problems and revealed premising results in processing time and solution quality. In ABC, a colony of artificial bees search for rich artificial food sources; the optimizing numerical problems are converted to the p...

Large quantities of data are emerging every year and an accurate clustering algorithm is needed to derive information from these data. K-means clustering algorithm is popular and simple, but has many limitations like its sensitivity to initialization, provides local optimum solutions. K-harmonic means clustering is an improved variant of K-means which is insensitive to the initialization of cen...

The aim of this work is to study the effect of hybridization on the performance of the Artificial Bee Colony (ABC) as a recently introduced metaheuristic for solving Resource Constrained Project Scheduling Problem (RCPSP). For this purposes, the ABC is combined with a Genetic Algorithm (GA). At the initial time, the algorithm generates a set of schedules randomly. The initial solution has been ...

We study the class of state-space models (or hidden Markov models) and perform maximum likelihood inference on the model parameters. We consider a stochastic approximation expectation-maximization (SAEM) algorithm to maximize the likelihood function with the novelty of using approximate Bayesian computation (ABC) within SAEM. The task is to provide each iteration of SAEM with a filtered state o...

QUESTIONS UNDER STUDY After years of advocating ABC (Airway-Breathing-Circulation), current guidelines of cardiopulmonary resuscitation (CPR) recommend CAB (Circulation-Airway-Breathing). This trial compared ABC with CAB as initial approach to CPR from the arrival of rescuers until the completion of the first resuscitation cycle. METHODS 108 teams, consisting of two physicians each, were rand...

Approximate Bayesian computation (ABC) have become an essential tool for the analysis of complex stochastic models. Grelaud et al. [(2009) Bayesian Anal 3:427-442] advocated the use of ABC for model choice in the specific case of Gibbs random fields, relying on an intermodel sufficiency property to show that the approximation was legitimate. We implemented ABC model choice in a wide range of ph...

Artificial Bee Colony (ABC) algorithm is a relatively new swarm intelligence algorithm which has shown a competitive performance with respect to other population-based algorithms. However, there are still some insufficiencies in ABC algorithm such as slow convergence and easily trapped in local optima. These drawbacks can be even more challenging when constraints are also involved. To address t...