# A Novel Adaptive Sparrow Search Algorithm Based on Chaotic Mapping and T-Distribution Mutation
## 1 Introduction  
In recent decades, in order to solve some complex optimization problems, many bionic swarm intelligence optimization algorithms have emerged, such as the ant colony algorithm (ACO), particle swarm optimization (PSO), artificial bee colony algorithm (ABC), genetic algorithm (GA), sparrow search algorithm (SSA), grey wolf optimization (GWO), differential evolution (DE), and so on [1; 2; 3; 4; 5]. The principle of these algorithms is simple and easy to implement, so they have attracted more and more attention [6; 7; 8; 9]. [PERSON] et al. [10] proposed an adaptive ant colony optimization algorithm based on an optimized guidance search mechanism to effectively solve the dynamic traveling salesman problem. [PERSON] et al. [11] proposed a speed-free multi-swarm particle swarm optimization, which overcomes the premature convergence caused by the limited search operator and the direction information used to guide the search process, and improved the stability. [PERSON] et al. [12] proposed an improved artificial bee colony algorithm (IABC). The proposed IABC uses a two-level encoding and machine-selected decoding method combined with a hybridsearch strategy of simulated annealing to maintain the diversity of the solution space and improve computational efficiency. [PERSON] et al. [13] proposed a multi-independent population genetic algorithm (MGA) combined with a subdomain model to improve the magnetic field distribution, cost, and efficiency of SPMSM, which proves the advantages of the MGA optimization method. In addition, some new algorithms also have been proposed in recent years [14; 15; 16; 17; 18; 19; 20; 21; 22; 23]. As a new algorithm of bionic swarm intelligence, the sparrow search algorithm was proposed by [PERSON] and [PERSON] [24] in 2020. It was inspired by sparrow foraging behavior, anti-predation behavior, and swarm intelligence. Compared with some other swarm intelligence algorithms, the SSA has stronger optimization capabilities and faster search efficiency, good stability, and strong robustness. It has been used in UAV trajectory planning [25] and image segmentation [26]. When the algorithm search is close to the global optimum, there will be problems, such as reduced population diversity and ease of falling into local optimum. In order to overcome these shortcomings, many scholars have carried out extensive research work to improve the optimization performance of the SSA. Their work mainly includes population initialization, designing mutation strategies, and combining other optimization algorithms. A single algorithm cannot solve all types of problems. A hybrid mechanism can be used to integrate the advantages of other algorithms and introduce them into the sparrow search algorithm, making full use of their respective advantages to search collaboratively to enhance the overall optimization capability of the SSA. [PERSON] et al. [27] introduced a good point set theory in number theory to improve the SSA algorithm, so that the initial population is uniformly and evenly distributed in the solution space and the convergence speed is improved. [PERSON] et al. [28] designed a chaotic sparrow search algorithm to enhance the global optimization capability for stochastic configuration networks. [PERSON] et al. [29] proposed a new balanced sparrow search algorithm to improve the efficiency of the CNN concerning consistency and accuracy. [PERSON] et al. [30] proposed a modified sparrow search algorithm to deal with the unmanned aerial vehicle route planning problem. [PERSON] et al. [31] proposed an improved sparrow search algorithm with three new strategies for a bioinspired path planning approach for mobile robots. [PERSON] et al. [32] proposed an improved sparrow search algorithm for a hydrological neural network hybrid model. [PERSON] et al. [33] proposed a new intelligent optimization algorithm called the sparrow search algorithm (SSA) and its modification for the electromagnetics and antenna community.