Social networks clustering is an NP-hard problem because it is difficult to find the communities in a reasonable time; therefore, the solutions are based on heuristics. Social networks clustering aims to collect people with common interest in one group. Several approaches have been developed for clustering social networks. In this paper the researchers, introduce a new approach to cluster socia...

This paper presents a novel optimization algorithm, namely hierarchical artificial bee colony optimization (HABC) for multilevel threshold image segmentation, which employs a pool of optimal foraging strategies to extends the classical artificial bee colony framework to a cooperative and hierarchical fashion. In the proposed hierarchical model, the higher-level species incorporates the enhanced...

In the present study, in order to predict the activity coefficient of inorganic ions, 12 cases of aqueous chloride solution were considered (AClx=1,2; A=Li, Na, K, Rb, Mg, Ca, Ba, Mn, Fe, Co, Ni). For this study, the UNIQUAC thermodynamic model is desired and its adjustable parameters are optimized with the Genetic + PSO algorithm. The optimization of the UNIQUAC model with PSO+ genetic algorit...

In order to solve problems of optimization, Swarm Intelligence (SI) algorithms are extensively becoming more popular. Many swarm intelligence based optimization techniques are present but most face problems like convergence problem and local minimization problem. In this paper, a hybrid optimization algorithm is proposed using fractional order Artificial Bee Colony (ABC) and Genetic Algorithm (...

In this paper, the previously proposed Predictive Energy Efficient Bee-inspired Routing (PEEBR) family of routing optimization algorithms based on the Artificial Bees Colony (ABC) Optimization model is extended from a random static mobility model, as employed by its first version (PEEBR1), into a random mobility model in its second version (PEEBR2). This random mobility model used by PEEBR-2 al...

This paper introduces a comparison of training algorithms of radial basis function (RBF) neural networks for classification purposes. RBF networks provide effective solutions in many science and engineering fields. They are especially popular in the pattern classification and signal processing areas. Several algorithms have been proposed for training RBF networks. The Artificial Bee Colony (ABC...

In the field of computer science and operation’s research, Artificial Bee Colony (ABC) is an optimization algorithm relatively new swarm intelligence technique based on behaviour of honey bee swarm and Meta heuristic. It is successfully applied to various paths mostly continuous optimization problems. Swarm intelligence systems are typically made up of a population of simple agents or boids int...

Artificial Bee Colony (ABC) is a distinguished optimization strategy that can resolve nonlinear and multifaceted problems. It is comparatively a straightforward and modern population based probabilistic approach for comprehensive optimization. In the vein of the other population based algorithms, ABC is moreover computationally classy due to its slow nature of search procedure. The solution exp...

Nature inspired metaheuristics proved to be very successful when applied to hard optimization problems, combinatorial as well as global. For all these algorithms, with very different basic ideas, parameters and implementation details, the common problem that ultimately determines the successfulness of a particular algorithm is balance between exploitation and exploration. Exploitation refers to...

Voting algorithms are extensively used to make decisions in fault tolerant systems where each redundant module gives inconsistent outputs. Popular voting algorithms include majority voting, weighted voting, and inexact majority voters. Each of these techniques suffers from scenarios where agreements do not exist for the given voter inputs. This has been successfully overcome in literature using...