Various algorithms have proposed during the last decade for solving different complex optimization problems. The meta-heuristic algorithms have been highly noted among researchers. In this paper, a new algorithm, known as the Buzzards Optimization algorithm (BUZOA), is introduced. Marvelous and special lifestyle of buzzards and their competition characteristics for prey has been the basic motivation for this new optimization algorithm. The algorithm performance has been compared with newest and well-known meta-heuristics on some benchmark problems and test functions. Results have shown the high performance of the proposed BUZOA compared to the other well known algorithms.

Nature-inspired metaheuristic algorithms have been a topic of interest for researchers to solve optimization problems in engineering designs and real-world applications, due to their simplicity and exibility. This paper presents a new Nature-inspired search algorithm called Woodpecker Mating algorithm (WMA) and applies it to challenging problems in structural optimization. The WMA is a population-based metaheuristic algorithm that mimics the mating behavior of woodpeckers. It was inspired by the drumming sound intensity. In WMA, the population of woodpeckers is divided into male and female groups. The female woodpeckers approach the male woodpeckers based on the intensity of their drum sound. An e ciency comparison was drawn between the WMA algorithm and other metaheuristic algorithms by employing 19 benchmark functions(including unimodal, multimodal and composite functions). Moreover, the performance of WMA is compared with 8 of the best meta-heuristic algorithms using 13 high dimensional multimodal and unimodal benchmark functions. The assessments and statistical results indicate that the WMA algorithm o ers promising results and is capable of outperforming the most recent and popular algorithms proposed in the literature in most of the employed benchmark functions. Moreover, a statistically signi cant di erence was observed compared to the other assessed algorithms. The proposed algorithm produced signi cant results for a non-convex, inseparable, and scalable problems.

Over the recent years, many research has been carried out on applying the optimization approach to science and engineering problems. Thereby, numerous metaheuristic algorithms have been developed for solving such type of challenge. Despite an increase in the number of these algorithms, there is currently no specific algorithm which can be employed to optimize all varieties of problems. In the current research, a novel metaheuristic algorithm for global and continuous nonlinear optimization, named as Xerus Optimization algorithm (XOA) has been introduced. XOA has been inspired by group living and lifestyle of cape ground squirrels (Xerus inauris), by taking into account their co-operation in living together, hunting, and communication, etc. In order to evaluate the e ciency of XOA, algorithms for 30 di erent benchmarks have been analyzed and compared to some novel and renowned metaheuristic algorithms. The simulation response illustrates a significant improvement in

Human beings have always made their tools and instruments they need using patterns in Nature. Mimicking Nature has become the foundation of a new science called Biomimetics. In the present article, multiple forms and levels in Nature were utilized to design and create a mouse. The rivers are a good source for choosing the shape of a mouse with lots of stones abraded through the centuries which also have smooth surfaces. In this research, a significant number of stones fitted to hand size were collected and then the best ones were scanned by an optical scanner. The point cloud model obtained was used to design and create the mouse and determine the geometric parameters of the mouse. After extracting the 3D model of the point cloud using a rapid prototyping technique with the Fused Deposition Modeling (FDM) method, some mouse models were designed ambidextrously for left-handed and right-handed people. Considering the results of the mouse evaluation by 30 people who were provided with the mouse, it can be concluded that the created mouse provided a high rate of satisfaction.

In recent times, consumption of non-renewable energy sources has been growing, because of the rise in population. The persistent exploitation of the conventional energy sources like fossil fuels, etc. led to insufficiency in the energy sources.Thus, the researchers detected an extra energy sourcenamed Renewable Energy Source, which serve as a best alternative for the conventional energy sources. Solar is considered to be the efficient one, because of its easy availability and pollution free Nature among the Renewable Energy Sourceslike. The usage of power electronic converters is required due to it is hard to obtain constant voltage from Photovoltaic system because of its sporadic Nature. Thus, the paper develops a comprehensive analysis of distinct Nature inspired optimization algorithm utilized to improve the performance of Photovoltaic system. Detecting the difficulties faced by the sporadic Nature of solar energy and limitations of conventional maximum power point tracking approaches under dynamic and partial shading conditions, the research evaluates many optimization algorithms. Moreover, the integration of advanced algorithms, serves as a development of more effective and adaptive optimization approaches. Additionally, the paper deliberates the benefits, limits and potential areas for future research of each optimization algorithm in the circumstance of Photovoltaic system performance improvement.