Metaheuristic algorithms are optimization algorithms that are used to address complicated issues that cannot be solved using standard approaches. These algorithms are inspired by natural processes such as genetics, swarm behavior, and evolution, and they are used to explore a broad search space to identify the global optimum of a problem. Genetic algorithms, particle swarm optimization, ant colony optimization, simulated annealing, and tabu search are examples of popular metaheuristic algorithms. These algorithms have been widely utilized to address complicated issues in domains like as engineering, finance, and computer science. In general, the history of metaheuristic algorithms spans several decades and involves the development of various optimization algorithms that are inspired by natural systems. Metaheuristic algorithms have become a valuable tool in solving complex optimization problems in various fields, and they are likely to continue to play an important role in the development of new technologies and applications.

Metaheuristic algorithms have been an interesting and widely used area for scientists, researchers and academicians because of their specific and significant characteristics and capabilities in solving optimization problems. Metaheuristic algorithms are developed base on inspiration of some real world phenomenon in nature or on the behavior of living being (animal, insects, organic living beings). On the past many metaheuristic algorithms have been introduced and applied on various problems of various domains including real world optimization problems. This paper is aimed to provide a historical Survey on metaheuristic algorithms, it will provide a list of metaheuristic based algorithms ordered according to the foundation year, with the name of Authors and the algorithm abbreviations.Â

Bio-Inspired optimization algorithms are inspired from principles of natural biological evolution and distributed collective of a living organism such as (insects, animal, …. etc.) for obtaining the optimal possible solutions for hard and complex optimization problems. In computer science Bio-Inspired optimization algorithms have been broadly used because of their exhibits extremely diverse, robust, dynamic, complex and fascinating phenomenon as compared to other existing classical techniques.This paper presents an overview study on the taxonomy of bio-inspired optimization algorithms according to the biological field that are inspired from and the areas where these algorithms have been successfully applied

This paper provides an introduction and a comparison of two widely used evolutionary computation algorithms: Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) based on the previous studies and researches. It describes Genetic Algorithm basic functionalities including various steps such as selection, crossover, and mutation. Â

Generally, Metaheuristic algorithms such as ant colony optimization, Elephant herding algorithm, particle swarm optimization, bat algorithms becomes a powerful methods for solving optimization problems. This paper provides a timely review of the bat algorithm and its new variants.Bat algorithm (BA) is a Swarm based metaheuristic algorithm developed in 2010 by Xin-She Yang, BA has been inspired by the foraging behavior of micro bats, algorithm carries out the search process using artificial bats as search agents mimicking the natural pulse loudness and emission rate of real bats. It has become a powerful swarm intelligence method for solving optimization prob-lems over continuous and discrete spaces. Nowadays, it has been successfully applied to solve problems in almost all areas of opti-mization, and it found to be very efficient. As a result, the literature has expanded significantly, a wide range of diverse applications and case studies has been made base on the bat algorithm.Â