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.

Metaheuristic optimization algorithms are a relatively new class of optimization algorithms that are widely used for difficult optimization problems in which classic methods cannot be applied and are considered as known and very broad methods for crucial optimization problems. In this study, a new Metaheuristic optimization algorithm is presented, the main idea of which is inspired by models in kinematics. This algorithm obtains better results compared to other optimization algorithms in this field and is able to explore new paths in its search for desirable points. Hence, after introducing the projectiles optimization (PRO) algorithm, in the first experiment, it is evaluated by the determined test functions of the IEEE congress on evolutionary computation (CEC) and compared with the known and powerful algorithms of this field. In the second try out, the performance of the PRO algorithm is measured in two practical applications, one for the training of the multi-layer perceptron (MLP) neural networks and the other for pattern recognition by Gaussian mixture modeling (GMM). The results of these comparisons are presented in various tables and figures. Based on the presented results, the accuracy and performance of the PRO algorithm are much higher than other existing methods.

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

optimization algorithms inspired by nature as intelligent optimization methods with classical methods have demonstrated significant success. Some of these techniques are genetic algorithms, inspired by biological evolution of humans and other creatures) ant colony optimization and simulated annealing method (inspired by the refrigeration process metals). The methods for solving optimization problems in many different areas such as determining the optimal course of their work, designing optimal control for industrial processes, solving industrial engineering major issues such as the optimal layout design for industrial units, problem solving, and queuing in the design of intelligent agents have been used. This paper introduces a new algorithm for optimization, which is not a natural phenomenon, but a phenomenon inspired teaching-human. It is entitled Education System algorithm (ESA). Results demonstrate this method is better than other method in this area.

The primary objective of this paper is to propose a novel technique for hybridizing various Metaheuristic algorithms to optimize the size of discrete structures. To accomplish this goal, two well-known Metaheuristic algorithms, particle swarm optimization (PSO) and enhanced colliding bodies optimization (ECBO) are hybridized to propose a new algorithm called hybrid PSO-ECBO (HPE) algorithm. The performance of the new HPE algorithm is investigated in solving the challenging structural optimization problems of discrete steel trusses and an improvement in results has been achieved. The numerical results demonstrate the superiority of the proposed HPE algorithm over the original versions of PSO, ECBO, and some other algorithms in the literature.

An optimal artificial neural network (ANN) has been developed to predict the Nusselt number of non-Newtonian nanofluids. The resulting ANN is a multi-layer perceptron with two hidden layers consisting of six and nine neurons, respectively. The tangent sigmoid transfer function is the best for both hidden layers and the linear transfer function is the best transfer function for the output layer. The network was trained by a particle swarm optimization (PSO) algorithm. Nanofluid concentration, Reynolds number, and Prandtl number are input for the ANN and the nanofluid Nusselt number is its output. There exists an excellent agreement between the ANN predicted values and experimental data. The average and maximum differences between experimental data and those predicted by ANN are about 0. 8 and 5. 6 %, respectively. It was also found that ANN predicts the Nusselt number of nanofluids more accurately than the previously proposed correlation.