###### Abstract
In recent years, researchers have proposed many new metaheuristic algorithms to solve the problem of parameter identification, which are similar to the Tuna Swarm Optimization (TSO) [7], Flower Pollination Algorithm, (FPA) [8], Bat Algorithm (BA) [9], Harmony Search (HS) [10], Squirrel Search Algorithm (SSA). All of these algorithms have been added to the MPPT application. Ref. [11] combined an optimized neural network trained through Deep Reinforcement Learning (DRL) with a classical closed-loop control and proved that this method can effectively improve the performance of MPPT. Literature [12] applied the FPA method to MPPT and achieved good results. In addition, there is a new trend in improving metaheuristic algorithms to solve the problem of multimodal objective functions. For example, [13] proposes to improve the performance of MPPT by reducing the random number in the CS. An MPPT algorithm for fast-changing shadows was proposed by [14], which showed good performance in reducing the algorithm oscillation frequency. Ref. [15] proposed to improve the Grey Wolf Algorithm by adding a Levy flight strategy. Ref. [16] proposed the Tunicate Swarm Algorithm (TSA) in an MPPT Control. Ref. [17] proposed the novel Musical Chairs Algorithm (MCA), which performs well in convergence time and reducing steady-state oscillations. In the study [18], by improving the tracking method of the CS algorithm, the tracking accuracy of the MPPT was effectively improved and the oscillation amplitude was reduced. Another noteworthy method is to mix different algorithms to improve the efficiency of MPPT. Literature [19] proposed a new hybrid TSA-PSO algorithm that combines the Tunicate Swarm Algorithm (TSA) with the particle swarm optimization (PSO) technique for efficient maximum power in PSCs. Ref. [20] combined the Cuckoo Algorithm with Grey Wolf Optimization, and the tracking accuracy of the MPPT was improved successfully. Ref. [21] presents a new hybrid MPPT technique by combining the Artificial Bee Colony (ABC) and P&O algorithms. This technique accelerates the convergence speed of the algorithm. In the article [22], Considering the Sliding mode control structure, the improved PSO (MPSO) algorithm is combined with P&O to effectively improve the convergence speed and stability of the tracking process. Research in [23] combines an improved Artificial Bee Colony algorithm with the Multiple Heat Transfer Search algorithm, greatly improving the accuracy of algorithm tracking. The noteworthy MPPT algorithm features found in a literature review are shown in Table 1.  
TSO, as a new metaheuristic algorithm with a fast dynamic response, has been used in MPPT. The TSO algorithm has the advantages of good stability and relatively few algorithm parameters. Moreover, the algorithm has great potential for improvement in tracking speed and convergence time. Improving the TSO algorithm has the potential to effectively reduce the oscillation amplitude and improve the algorithm convergence speed. The TSO algorithm also has the potential to further optimize the dynamic response speed. In this paper, the Levy flight strategy was added to the TSO algorithm to improve the ability of the algorithm to jump out of the previous LMPP. Due to the fixed weight coefficient of the TSO algorithm, the global stable convergence cannot be considered. The introduction of an adaptive weight coefficient can effectively improve the local convergence performance of the TSO algorithm. During the algorithm initialization stage, the search