## 1 Introduction
The semidefinite programming (SDP), which transforms the multimodal optimization problem into a convex one by applying the corresponding relaxations, is commonly used [2]. It has been demonstrated, however, that the SDP approach has some drawbacks that impact the accuracy of the global optimal solution, particularly when the measurement noise level is high [14]. In this context, obtaining a high precision solution that represents global optimum to the nonlinear, non-convex ML objective function has become a formidable obstacle. This paper presents evolutionary algorithms (EAs) as a way to overcome these limitations while searching for the global optimum solution of a multimodal and complex target estimation problem [15; 16; 17]. In the development of the EAs, the exploration and exploitation phases have a crucial impact on the obtained solutions [18]. EAs perform exploration in searching for a promising location of the global optimum, whereas exploitation is performed when looking to improve the obtained global best solution. Therefore, to achieve a promising solution, the optimization algorithm must provide an effective balance between these two phases. Various metaheuristic algorithms, such as the genetic algorithm (GA) [19], particle swarm optimization (PSO) [20], butterfly optimization algorithm (BOA) [21], differential evolutionary (DE) [22], cuckoo search algorithm (CS) [23], artificial bee colony (ABC) [24], gray wolf optimizer (GWO) [25], whale optimization algorithm (WOA) [26], and firefly algorithm (FA) [27], have been developed and applied to various optimization problems using key characteristics of these biological systems. BOA and DE algorithms have been successfully used in recent years to identify the global optimal solution to a variety of complex optimization problems due to their advantages such as ease of development and fast convergence [28; 29; 30]. Differential evolution is a stochastic population-based evolutionary algorithm that employs the recombination approach, which creates new solutions depending upon the weighted difference between two randomly selected population members added to a third population member to direct the search in the solution space. [PERSON] and [PERSON] [22] first proposed this method for minimizing nonlinear functions that are not differentiable. Sincethen, DE has proven to be highly effective in a wide range of scientific optimization problems thanks to its ability to prevent premature and encourage rapid convergence as well as guarantee population diversity [16; 31]. Like other EAs, it employs a population of candidate solutions and searches stochastically by applying mutation, crossover, and selection operators to guide the population toward the promising solutions. The simplicity of conventional DE lies in the fact that the crossover rate, scale factor, and population size are the only three control parameters that need to be adjusted. The mutation technique and the selection of the control parameters have a significant impact on the efficiency with which DE solves a given optimization problem [28]. In certain optimization situations, DE is prone to converge prematurely to local optima and exhibits poor convergence late in the search process, motivating researchers to develop new enhanced variants of the DE algorithm to improve the optimization performance of the DE to overcome these challenges [32; 33]. There are a variety of mutation strategies in the literature. This is because the choice of mutation strategy has a strong effect on how well an optimization problem is solved. In this way, DE/rand/1 and DE/rand/2 are better for exploration, whereas the DE/current-to-best/1 and DE/current-to-best/2 are better for local searches because they focus on the best candidate in the current population [28]. To boost optimization effectiveness, a new DE algorithm, JADE [33], has been proposed, with a new mutation strategy, \"DE/current-to-p best\" as an extension to the traditional \"DE/current-to-best/1\", along with an optional external archive.