nature inspired algorithms

Harris Hawks Optimization (HHO) algorithm, which is a new metaheuristic algorithm that has shown promising results in comparison to other optimization methods. The surprise pounce is a cooperative behavior and chasing style exhibited by Harris' Hawks in nature. To address the limitations of HHO, specifically its susceptibility to local optima and lack of population diversity, a modified version called Modified Harris Hawks Optimization (MHHO) is proposed for solving global optimization problems. A mutation-selection approach is utilized in the proposed Modified Harris Hawks Optimization (MHHO) algorithm. Through systematic experiments conducted on 23 benchmark functions, the results have demonstrated that the MHHO algorithm offers a more reliable solution compared to other established algorithms. The MHHO algorithm exhibits superior performance to the basic HHO, as evidenced by its superior average values and standard deviations. Additionally, it achieves the smallest average values among other algorithms while also improving the convergence speed. The experiments demonstrate competitive results compared to other meta-heuristic algorithms, which provide evidence that MHHO outperforms others in terms of optimization performance.

It is important to calculate the optimum design parameters of synchronous generator (SG) to obtain the desired total harmonic distortion (THD). In this study, we aim to determine the optimum rotor design parameters of SG by using grey wolf optimizer (GWO) algorithm. For this purpose regression modelling is performed to mathematically modelling the relationship between the selected rotor design parameters (factors namely slot pitch, center slot pitch, and damper width) and THD (response). This factor combination is not previously investigated in the related literature. Then by using GWO the optimization is performed on this regression equation. Maxwell simulations have been used for numerical experiments. The results of GWO are compared with the results of genetic algorithm (GA). The results indicate that the GWO algorithm can be well adapted to similar optimization processes and can be effectively used. As a result, the voltage THD of the SG is reduced to 0.3951 under the acceptable magnetic flux conditions. This GWO aided optimization study is significant in that it demonstrates how the performance of SG can be improved by making minor changes to the production line that has been adjusted for mass production without changing the outer diameter and dimensions of SG.

Researchers in many fields, such as operations research, computer science, AI engineering, and mathematical engineering, extra, are increasingly adopting nature-inspired metaheuristic algorithms because of their simplicity and flexibility. Natural metaheuristic algorithms are based on two essential terms: exploration (diversification) and exploitation (intensification). The success and limitations of these algorithms are reliant on the tuning and control of their parameters. When it comes to tackling real optimization problems, the Gorilla Troop Optimizer (GTO) is an extremely effective algorithm that is inspired by the social behavior of gorilla troops. Three operators of the original GTO algorithm are committed to exploration, and the other two operators are dedicated to exploitation. Even though the superiority of GTO algorithm to several metaheuristic algorithms, it needs to improve the balance between the exploration process and the exploitation process to ensure an accurate estimate of the global optimum. For this reason, a Novel Enhanced version of GTO (NEGTO), which focuses on the correct balance of exploration and exploitation, has been proposed. This paper suggests a novel modification on the original GTO to enhance the exploration process and exploitation process respectively, through introducing a dynamic controlling parameter and improving some equations in the original algorithm based on the new controlling parameter. A computational experiment is conducted on a set of well-known benchmark test functions used to show that NEGTO outperforms the standard GTO and other well-known algorithms in terms of efficiency, effectiveness, and stability. The proposed NEGTO for solving global optimization problems outperforms the original GTO in most unimodal benchmark test functions and most multimodal benchmark test functions, a wider search space and more intensification search of the global optimal solution are the main advantages of the proposed NEGTO.

VANETs are a subset of MANETs in which vehicles are considered as network nodes. These networks have been created to communicate between vehicle and traffic control on the roads. VANETs have similar features to MANETs, and their main special property is the high-speed node mobility which makes a quick-change topology in the network. The rapid change of network topology is a major challenge in routing. One of the well-known routing protocols in VANETs is the AODV routing protocol. In this inquiry, nature-inspired algorithms such as GOA and GA are used to improve routing in VANETs to search the optimal configuration of the AODV routing protocol, and its impact on network evaluation criteria has been investigated. The rating measures applied in this research are the packet delivery ratio, end-to-end delays, and normalized routing load.