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. Manuscript profile

Recently, many optimization algorithms have been proposed to find the best solution for complex engineering problems. These algorithms can search unknown and multidimensional spaces and find the optimal solution the shortest possible time. In this paper we present a new modified differential evolution algorithm. Optimization algorithms typically have two stages of exploration and exploitation. Exploration refers to global search and exploitation refers to local search. We used the same differential evolution (DE) algorithm. This algorithm uses a random selection of several other search agents to update the new search agent position. This makes the search agents continually have random moves in the search space, which refers to the exploration phase but there is no mechanism specifically considered for the exploitation phase in the DE algorithm. In this paper, we have added a new formula for the exploitation phase to this algorithm and named it the Balanced Differential Evolution (BDE) algorithm. We tested the performance of the proposed algorithm on standard test functions, CEC2005 Complex and Combined Test Functions. We also apply the proposed algorithm to solve some real problems to demonstrate its ability to solve constraint problems. The results showed that the proposed algorithm has a better performance and competitive performance than the new and novel optimization algorithms. Manuscript profile