Best-so-far ABC is a modified version of the artificial bee colony (ABC) algorithm used for optimization tasks. This algorithm is one of the swarm intelligence (SI) algorithms proposed in recent literature, in which the results demonstrated that the best-so-far ABC can produce higher quality solutions with faster convergence than either the ordinary ABC or the current state-of-the-art ABC-based...

Logitboost is an influential boosting algorithm for classification. In this paper, we develop robust logitboost to provide an explicit formulation of tree-split criterion for building weak learners (regression trees) for logitboost. This formulation leads to a numerically stable implementation of logitboost. We then propose abc-logitboost for multi-class classification, by combining robust logi...

The atom-bond connectivity index—abbreviated ABC—has proven to describe the heat of formation of alkanes with an accuracy comparable to that of high-level ab initio and DFTmethods. Its physical interpretation in terms of quantumtheory, its generalization, and many of its mathematical properties have been reported. Here, a probabilistic interpretation of the generalized ABC index is provided. It...

The quest for a more powerful method for model evaluation has inspired Vrugt and Sadegh (2013) to introduce ‘‘likelihood-free’’ inference as vehicle for diagnostic model evaluation. This class of methods is also referred to as Approximate Bayesian Computation (ABC) and relaxes the need for a residualbased likelihood function in favor of one or multiple different summary statistics that exhibit ...

Artificial Bee Colony (ABC) algorithm is a relatively new swarm intelligence algorithm that has attracted great deal of attention from researchers in recent years with the advantage of less control parameters and strong global optimization ability. However, there is still an insufficiency in ABC regarding its solution search equation, which is good at exploration but poor at exploitation. This ...

parameter estimation of the nonlinear muskingum model is a highly nonlinear optimization problem. although various techniques have been applied to optimize the coefficients of the nonlinear muskingum flood routing models, but an efficient method for this purpose in the calibration process is still lacking. the accuracy of artificial bee colony (abc) algorithm is investigated in this paper to op...

objectives: the aim of this study was to evaluate the psychometric features of the persian version of the autism behavior checklist (abc). method: the international quality of life assessment (iqola) approach was used to translate the english abc into persian. a total sample of 184 parents of children including 114 children with autism disorder (mean age =7.21, sd =1.65) and 70 typically develo...

This paper reports on a case study that describes the implementation of activity-based costing (ABC) for a Taiwan communication company. Through an in-depth field investigation over a communicationequipment factory, the ABC was invoked to analyze the cost generated by the actual production processes in the present study. The company’s existing cost system adopts a rough estimate based to alloca...

Artificial Bee Colony (ABC) is a swarm optimization technique. This algorithm generally used to solve nonlinear and complex problems. ABC is one of the simplest and up to date population based probabilistic strategy for global optimization. Analogous to other population based algorithms, ABC also has some drawbacks computationally pricey due to its sluggish temperament of search procedure. The ...