Swarm Intelligence is a metaheuristic optimization approach that has become very predominant over the last few decades. These algorithms are inspired by animals' physical behaviors and their evolutionary perceptions. The simplicity of these algorithms allows researchers to simulate different natural phenomena to solve various real-world problems. This paper suggests a novel algorithm called Donkey and Smuggler Optimization Algorithm (DSO). The DSO is inspired by the searching behavior of donkeys. The algorithm imitates transportation behavior such as searching and selecting routes for movement by donkeys in the actual world. Two modes are established for implementing the search behavior and route-selection in this algorithm. These are the Smuggler and Donkeys. In the Smuggler mode, all the possible paths are discovered and the shortest path is then found. In the Donkeys mode, several donkey behaviors are utilized such as Run, Face & Suicide, and Face & Support. Real world data and applications are used to test the algorithm. The experimental results consisted of two parts, firstly, we used the standard benchmark test functions to evaluate the performance of the algorithm in respect to the most popular and the state of the art algorithms. Secondly, the DSO is adapted and implemented on three real-world applications namely; traveling salesman problem, packet routing, and ambulance routing. The experimental results of DSO on these real-world problems are very promising. The results exhibit that the suggested DSO is appropriate to tackle other unfamiliar search spaces and complex problems.

At present, deep learning is widely used in a broad range of arenas. A convolutional neural networks (CNN) is becoming the star of deep learning as it gives the best and most precise results when cracking real-world problems. In this work, a brief description of the applications of CNNs in two areas will be presented: First, in computer vision, generally, that is, scene labeling, face recognition, action recognition, and image classification; Second, in natural language processing, that is, the fields of speech recognition and text classification.

Stemming is one of the most significant preprocessing. stages in text categorization that most of the academic investigators aim to improve and optimize the accuracy of the classification task. High dimensionality of feature space is one of the challenges in text classification that can be decreased by many techniques. In stemming, high dimensionality of feature space is decreased by grouping those words that they have same grammatical forms and then getting their root. This work is dedicated to build an approach for Kurdish language classification using Reber Stemmer. Thus, an innovative approach is investigated to get the stem of words in Kurdish language by removing longest suffix and prefixes of words. This approach has a strong capability and meets the requirements in responding to the process of deleting as many of the required affixes as possible to get the stem of words in Kurdish language. The advantage of this stemmer is that it ignores the ordering list of affixes that receives correct stem for more than one words that have the same format. The stemming technique is implemented on KDC-4007 dataset that consists of eight classes. Support Vector Machine (SVM) and Decision Tree (DT or C 4.5) are used for the classification. This stemmer has been successfully compared with the Longest-Match stemmer technique. According to results, the F-measure of Reber stemmer and Longest-Match method in SVM is higher than DT. Reber stemmer in SVM for classes (religion, sport, health and education) obtained higher F-measure, while the rest of classes are lower in Longest-Match. Reber stemmer in DT for classes (religion, sport and art) had higher F-measure for Reber stemmer while in Longest match the rest of classes showed lower F-measure.

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One popular example of metaheuristic algorithms from the swarm intelligence family is the Bat algorithm (BA). The algorithm was first presented in 2010 by Yang and quickly demonstrated its efficiency in comparison with other common algorithms. The BA is based on echolocation in bats. The BA uses automatic zooming to strike a balance between exploration and exploitation by imitating the deviations of the bat's pulse emission rate and loudness as it searches for prey. The BA maintains solution diversity using the frequency-tuning technique. In this way, the BA can quickly and efficiently switch from exploration to exploitation. Therefore, it becomes an efficient optimizer for any application when a quick solution is needed. In this paper, an improvement on the original BA has been made to speed up convergence and make the method more practical for large applications. To conduct a comprehensive comparative analysis between the original BA, the modified BA proposed in this paper, and other state-of-the-art bio-inspired metaheuristics, the performance of both approaches is evaluated on a standard set of 23 (unimodal, multimodal, and fixed-dimension multimodal) benchmark functions. Afterward, the modified BA was applied to solve a real-world job scheduling problem in hotels and restaurants. Based on the achieved performance metrics, the proposed MBA establishes better global search ability and convergence than the original BA and other approaches.