University of Kurdistan Hewler

About: University of Kurdistan Hewler is a education organization based out in Erbil, Iraq. It is known for research contribution in the topics: Computer science & Metaheuristic. The organization has 140 authors who have published 290 publications receiving 2104 citations. The organization is also known as: University of Kurdistan Hewler.

Fitness Dependent Optimizer: Inspired by the Bee Swarming Reproductive Process

Abstract: In this paper, a novel swarm intelligent algorithm is proposed, known as the fitness dependent optimizer (FDO). The bee swarming the reproductive process and their collective decision-making have inspired this algorithm; it has no algorithmic connection with the honey bee algorithm or the artificial bee colony algorithm. It is worth mentioning that the FDO is considered a particle swarm optimization (PSO)-based algorithm that updates the search agent position by adding velocity (pace). However, the FDO calculates velocity differently; it uses the problem fitness function value to produce weights, and these weights guide the search agents during both the exploration and exploitation phases. Throughout this paper, the FDO algorithm is presented, and the motivation behind the idea is explained. Moreover, the FDO is tested on a group of 19 classical benchmark test functions, and the results are compared with three well-known algorithms: PSO, the genetic algorithm (GA), and the dragonfly algorithm (DA); in addition, the FDO is tested on the IEEE Congress of Evolutionary Computation Benchmark Test Functions (CEC-C06, 2019 Competition) [1]. The results are compared with three modern algorithms: (DA), the whale optimization algorithm (WOA), and the salp swarm algorithm (SSA). The FDO results show better performance in most cases and comparative results in other cases. Furthermore, the results are statistically tested with the Wilcoxon rank-sum test to show the significance of the results. Likewise, the FDO stability in both the exploration and exploitation phases is verified and performance-proofed using different standard measurements. Finally, the FDO is applied to real-world applications as evidence of its feasibility.

Recent Developments in Sweat Analysis and Its Applications

Abstract: Currently, the clinical use of sweat as biofluid is limited. The collection of sweat and its analysis for determining ethanol, drugs, ions, and metals have been encompassed in this review article to assess the merits of sweat compared to other biofluids, for example, blood or urine. Moreover, sweat comprises various biomarkers of different diseases including cystic fibrosis and diabetes. Additionally, the normalization of sampled volume of sweat is also necessary for getting efficient and useful results.

A Systematic and Meta-Analysis Survey of Whale Optimization Algorithm

Abstract: The whale optimization algorithm (WOA) is a nature-inspired metaheuristic optimization algorithm, which was proposed by Mirjalili and Lewis in 2016. This algorithm has shown its ability to solve many problems. Comprehensive surveys have been conducted about some other nature-inspired algorithms, such as ABC and PSO. Nonetheless, no survey search work has been conducted on WOA. Therefore, in this paper, a systematic and meta-analysis survey of WOA is conducted to help researchers to use it in different areas or hybridize it with other common algorithms. Thus, WOA is presented in depth in terms of algorithmic backgrounds, its characteristics, limitations, modifications, hybridizations, and applications. Next, WOA performances are presented to solve different problems. Then, the statistical results of WOA modifications and hybridizations are established and compared with the most common optimization algorithms and WOA. The survey's results indicate that WOA performs better than other common algorithms in terms of convergence speed and balancing between exploration and exploitation. WOA modifications and hybridizations also perform well compared to WOA. In addition, our investigation paves a way to present a new technique by hybridizing both WOA and BAT algorithms. The BAT algorithm is used for the exploration phase, whereas the WOA algorithm is used for the exploitation phase. Finally, statistical results obtained from WOA-BAT are very competitive and better than WOA in 16 benchmarks functions. WOA-BAT also outperforms well in 13 functions from CEC2005 and 7 functions from CEC2019.