## 1 Introduction
[32] applied the GWO algorithm with fuzzy logic controllers to eliminate the oscillation near GMPP. [PERSON] et al. [33] examined the design of the MPPT circuit along with the GWO algorithm that mitigates the shortfalls of conventional tracking methods, such as poor performance in tracking, low efficiency and more oscillations near and around MPP. The key distinctions among this approach include their effectiveness range, convergence speed, design complexity, sensor requirements, control parameters and hardware implementation costs. Consequently, selecting an appropriate algorithm is crucial when designing a photovoltaic (PV) system, as it depends on the intended application. After the selection of the optimization algorithm, the adaptation for the solved optimization task must be done. This paper compares widely used MPPT techniques to mitigate the negative impact of partial shading, thereby enhancing maximum power output. It explores the advantages and disadvantages of these techniques and provides a general comparison of various solutions. This article particularly focuses on discussing different approaches for global maximum power point tracking (GMPPT) under partial shading conditions, with emphasis on recent advancements in the scientific literature. The main contributions of this paper are as follows:  
* A statistical investigation of modern heuristic optimization methods for (GMPPT) in photovoltaic (PV) systems under partial shading conditions has been presented;
* Comprehensive analysis of the challenges associated with heuristic optimization-based GMPPT techniques, focusing on their exploitative and explorative search capabilities;
* Introduction of a novel GMPPT method called Chimp Optimization Algorithm, which effectively balances the exploitative and explorative search capabilities;
* Statistical comparisons of different heuristic optimization-based GMPPT techniques in terms of tracking routines, accumulated energy and tracking efficiency. _Applications of Metaheuristics Algorithm_  
Metaheuristics algorithms offer a powerful approach to tackling complex optimization problems in diverse domains. Their flexibility, robustness and ability to find satisfactory solutions make them invaluable tools for real world problems as presented below. Metaheuristics are widely used to tackle problems with a large number of possible combinations, such as the Traveling Salesman Problem, Knapsack Problem or Job Scheduling. Examples of metaheuristics for combinatorial optimization include GA, PSO, ACO and Simulated Annealing (SA). Metaheuristics are used for optimizing the design parameters of complex systems. For example, they can optimize the shape of an aircraft wing, the layout of an electric circuit, electromagnetic device or the parameters of a chemical process. Metaheuristics are utilized in optimizing transportation routes, vehicle routing problems and logistics planning. They help find efficient paths for deliveries, minimizing travel time and costs. Metaheuristics can optimize production schedules, inventory management, and resource allocation in manufacturing processes. Metaheuristics can be used to create computer programs that can play games effectively by finding near-optimal strategies.