An Overview of the Concepts, Classifications, and Methods of Population Initialization in Metaheuristic Algorithms

Metaheuristic algorithms are typically population-based random search techniques. The general framework of a metaheuristic algorithm consisting of its main parts. The sections of a metaheuristic algorithm include setting algorithm parameters, population initialization, global search section, local search section, and checking the stopping conditions in a metaheuristic algorithm. In the parameters setting section, the user can monitor the performance of the metaheuristic algorithm and improve its performance according to the problem under consideration. In this study, an overview of the concepts, classifications, and different methods of population initialization in metaheuristic algorithms discussed in recent literature will be provided. Population initialization is a basic and common step between all metaheuristic algorithms. Therefore, in this study, an attempt has been made that the performance, methods, mechanisms, and categories of population initialization in metaheuristic algorithms. Also, the relationship between population initialization and other important parameters in performance and efficiency of metaheuristic algorithms such as search space size, population size, the maximum number of iteration, etc., which are mentioned and considered in the literature, are collected and presented in a regular format.

Keywords:

Classification , Clustering , metaheuristic algorithms , Optimization Algorithms