Clustering task aims at the unsupervised classification of patterns in different groups. To enhance the quality of results, the emerging swarm-based algorithms now-a-days become an alternative to the conventional clustering methods. In this study, an optimization method based on the swarm intelligence algorithm is proposed for the purpose of clustering. The significance of the proposed algorith...

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 technique based on the intelligent foraging behavior of honey bees. We present a 2-level metaheuristic approach where an Artificial Bee Colony Algorithm acts as a low-level metaheuristic and its paremeters are se...

The ABC algorithm has been used in many practical cases and has demonstrated good convergence rate. It produces the new solution according to the stochastic variance process. In this process, the magnitudes of the perturbation are important since it can affect the new solution. In this paper, we propose a self adaptive artificial bee colony, called self adaptive ABC, for the global numerical op...

The integration of non-orthogonal multiple access (NOMA) technology and cognitive radio networks (CRNs) promises to enhance the spectrum utilization efficiency 5G beyond-5G (B5G) mobile communication systems. In this article, a NOMA-based spectrum-sharing scheme is proposed for dual-hop CRNs in which primary transmitter separated by long distance from receiver communicates via CRN. scenario, we...

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...

In this paper, an improved artificial bee colony (I-ABC) algorithm for the maximum power point tracking (MPPT) of a photovoltaic module array (PVMA) is presented. Even though P-V output characteristic curve with multi-peak was generated due to any damages or shading discovered on PVMA, I-ABC could get rid stuck local (LMPP), but quickly and stably track global (GMPP), thereby improving generati...

FunctionalLinkNeuralNetwork(FLNN)hasemergedasanimportanttoolforsolvingnon-linear classificationproblemandhasbeensuccessfullyappliedinmanyengineeringandscientificproblems. TheFLNNstructureismuchmoremodestthanordinaryfeedforwardnetworkliketheMultilayer Perceptron (MLP)due to its flat network architecturewhich employs less tuneableweights for...

Artificial bee colony (ABC) algorithm is one of the most recently proposed swarm intelligence algorithms for global numerical optimization. It performs well in most cases; however, there still exist some problems it cannot solve very well. This paper presents a novel hybrid Hooke Jeeves ABC (HJABC) algorithm with intensification search based on the Hooke Jeeves pattern search and the ABC. The m...

Artificial Bee Colony (ABC) is a swarm-based metaheuristic for continuous optimization. Recent work hybridized this algorithm with other metaheuristics in order to improve performance. The work in this paper, experimentally evaluates the use of different mutation operators with the ABC algorithm. The introduced operator is activated according to a determined probability called mutation rate (MR...

Crime detection is one of the major issues in the field of criminology. In fact, criminology includes knowing the details of a crime and its intangible relations with the offender. In spite of the enormous amount of data on offenses and offenders, and the complex and intangible semantic relationships between this information, criminology has become one of the most important areas in the field o...