Combinatorial Bees Algorithm for Vehicle Routing Problem

Vehicle routing problem (VRP) is a combinatorial optimization problem that has been studied intensively for years. Although VRP can be solved using an exact method for small instances, a more complex one will be impractical due to the nature of the problem as an NP-hard problem. A metaheuristic is more suitable to solve this kind of problem because the near-optimal solution can be found in a relatively shorter time compared to the exact algorithm. Bees Algorithm (BA) as nature-inspired metaheuristic is used to find a near-optimal solution of VRP-TSPLIB’s datasets. This study aims to provide the results of BA on the standard dataset of VRP. The BA has a very good performance, with 3.9% of the average Best-Error and 1.2 million of the average evaluations to reach the solution

Cyberbullying detection framework for short and imbalanced Arabic datasets

Cyberbullying detection has attracted many researchers to detect negative comments deployed on communication platforms as cyberbullying can take many forms: verbal, implicit, explicit, or even nonverbal. The successful growth of social media in recent years has opened new perspectives on the detection of cyberbullying, although related research still encounters several challenges, such as data imbalance and expression implicitness. In this paper, we propose an automated cyberbullying detection framework designed to produce satisfactory results, especially when imbalanced short text and different dialects exist in the Arabic text data. In the proposed framework a new method to solve the imbalance problem is suggested, where the modified simulated annealing optimization algorithm is used to find the optimal set of samples from the majority class to balance the training set. This method has been evaluated using traditional machine learning algorithms including support vector machine, and deep learning algorithms including Long Short-Term Memory (LSTM) and Bidirectional LSTM (Bi-LSTM). To generate a framework that can detect Arabic written cyberbullying on communication platforms, the accuracy, recall, specificity, sensitivity and mean squared error are used as the main performance indicators. The results indicate that the proposed framework can improve the performance of the tested algorithms, and Bi-LSTM outperforms other methods for cyberbullying classification

Optimal job scheduling in grid computing using efficient binary artificial bee colony optimization

The artificial bee colony has the advantage of employing fewer control parameters compared with other population-based optimization algorithms. In this paper a binary artificial bee colony (BABC) algorithm is developed for binary integer job scheduling problems in grid computing. We further propose an efficient binary artificial bee colony extension of BABC that incorporates a flexible ranking strategy (FRS) to improve the balance between exploration and exploitation. The FRS is introduced to generate and use new solutions for diversified search in early generations and to speed up convergence in latter generations. Two variants are introduced to minimize the makepsan. In the first a fixed number of best solutions is employed with the FRS while in the second the number of the best solutions is reduced with each new generation. Simulation results for benchmark job scheduling problems show that the performance of our proposed methods is better than those alternatives such as genetic algorithms, simulated annealing and particle swarm optimization.Web of Science17588286