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.

This paper presents a model for minimizing the makespan in the flow shop scheduling problem. Due to the impact of increased workloads, flow shops are becoming more popular and widely used in industries. To solve the challenge of minimizing makespan, a Hybrid-Heuristic-Metaheuristic-Genetic-Algorithm (HHMGA) is proposed. The proposed HHMGA Algorithm is tested using the simulation software and demonstrated with steel industry data. The results are compared with those of the best available flow shop problem Algorithms such as Palmer’s slope index, Campbell-Dudek-Smith (CDS), Nawaz-Enscore-Ham (NEH), genetic Algorithm (GA) and particle swarm optimization (PSO). According to empirical results and relative differences from the lower bound, the proposed technique outperforms the three heuristics and two metaheuristics Algorithms in three of six cases, while the remaining three produce the same results as the NEH heuristic. In comparison to the steel industry's regular job scheduling technique, the simulation model based on HHMGA can save 4642 hours. It was discovered that the suggested model enhanced the job sequence based on the makespan requirements.

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.

Background and Objectives: This paper explores the realm of optimization by synergistically integrating two unique metaheuristic Algorithms: the Wild Horse Optimizer (WHO) and the Fireworks Algorithm (FWA). WHO, inspired by the behaviors of wild horses, demonstrates proficiency in global exploration, while FWA emulates the dynamic behavior of fireworks, thereby enhancing local exploitation. The goal is to harness the complementary strengths of these Algorithms, achieving a harmonious balance between exploration and exploitation to enhance overall optimization performance.Methods: The study introduces a novel hybrid metaheuristic Algorithm, WHOFWA, detailing its design and implementation. Emphasis is placed on the Algorithm's ability to balance exploration and exploitation. Extensive experiments, featuring a diverse set of benchmark optimization problems, including general test functions and those from CEC 2005, CEC 2019, and 2022, assess WHOFWA's effectiveness. Comparative analyses involve WHO, FWA, and other metaheuristic Algorithms such as Reptile Search Algorithm (RSA), Prairie Dog Optimization (PDO), Fick’s Law Optimization (FLA), and Ladybug Beetle Optimization (LBO).Results: According to the Friedman and Wilcoxon signed-rank tests, for all selected test functions, WHOFWA outperforms WHO, FWA, RSA, PDO, FLA, and LBO by 42%, 55%, 74%, 71%, 48%, and 52%, respectively. Finally, the results derived from addressing real-world constrained optimization problems using the proposed Algorithm demonstrate its superior performance when compared to several well-regarded Algorithms documented in the literature.Conclusion: In conclusion, WHOFWA, the hybrid metaheuristic Algorithm uniting WHO and FWA, emerges as a powerful optimization tool. Its unique ability to balance exploration and exploitation yields superior performance compared to WHO, FWA, and benchmark Algorithms. The study underscores WHOFWA's potential in tackling complex optimization problems, making a valuable contribution to the realm of metaheuristic Algorithms.

A new swarm intelligence optimization technique, called Artificial Coronary Circulation System (ACCS), is proposed in this study. This optimization method simulates the growth of coronary arteries (veins) in the human heart. In this Algorithm, each capillary is considered a candidate solution. This Algorithm starts with a random initial population of candidate solutions and evaluates them by using Coronary Growth Factor (CGF). In each run, the best candidate solution is selected as the main coronary vessel (artery or vein), and the other capillaries are considered as searchers of the search space. Then, the heart decides whether other candidates move toward/away from the main coronary vessels and searches for the optimal solution by using heart memory. Finally, the application of the proposed Algorithm is demonstrated with respect to some benchmark functions and some mechanical problems, confirming the potential and capability of the new Algorithm

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.