## 1 Introduction
[21] introduced a fractional-order chaotic improved SSA, demonstrating higher convergence accuracy than the SSA. [PERSON] et al. [22] implemented CAT chaotic mapping to initialize a population and explored the initial population's randomness and searchability. [PERSON] et al. [23] used sin chaotic mapping to initialize the sparrow population, aiming to improve the quality of initial solutions. [PERSON] et al. [24] introduced circular chaotic mapping into the SSA, enhancing the algorithm's capacity for global exploration during population initialization. They additionally introduced the t-distribution in the formula for updating the positions of sparrows across various iteration cycles, aiding the algorithm in avoiding local optima. The current research places significant emphasis on the population initialization process in the SSA since the population's quality distribution significantly influences the efficacy of the search for the global optimum. In the early stages of the algorithm, a well-designed population initialization process can effectively improve the algorithm's capability for global exploration. Chaos mapping is a frequently used approach in the existing literature on initialization, as mentioned above. However, chaotic systems are highly sensitive to starting conditions and display unpredictable randomness. Additionally, chaotic mapping algorithms involve complex mathematical operations, leading to increased computational costs. In this paper, we propose the Halton sequence for population initialization. In comparison to the aforementioned methods, the Halton sequence ensures a more uniformly distributed initial population across the entire space and lacks random components, making it more robust. Moreover, its lower sensitivity to initial conditions and parameters enhances its reliability in algorithm applications. The crossover operator plays a crucial role in optimization algorithms by promoting population diversity and exploring the search space. The Laplace crossover operator (LX) is a particularly unique crossover operator that has been applied in various fields [25; 26]. [PERSON] et al. [27] first introduced the LX in 2007 and applied it to genetic algorithms (GAs). Experimental results demonstrated that the LX-GA outperformed other types of GAs. Consequently, in many studies related to GAs, researchers tend to utilize the LX for performing crossover operations [28; 29]. The LX is suitable for optimization problems involving continuous parameters and generates random numbers based on the Laplace distribution, exhibiting good randomness and perturbation properties. Additionally, the basic operation of the LX is based on the Laplace distribution, requiring minimal parameter tuning. Considering the numerous advantages of the LX, in this study, we utilized the LX to disturb the position of the best individual at each iteration with the goal of improving the algorithm's ability for local exploration and preventing the population from becoming ensnared in local optima. The primary contributions of this paper are outlined as follows:  
1. In the sparrow initialization stage, cleverly utilizing the Halton sequence to generate initial solutions effectively improves the quality of the initial solutions, thereby significantly enhancing the algorithm's robustness. This initialization strategy not only aids in a more evenly distributed set of initial solutions across the entire solution space but also reduces the algorithm's sensitivity to initial conditions. 2. Introducing the Laplace crossover operator to cleverly perturb the position of the best individual in each iteration successfully mitigates the possibility of the algorithm becoming ensnared in local optima while significantly improving the convergence speed. By incorporating this effective local search strategy during the optimization process, we effectively expanded the algorithm's exploration range in the solution space. 3. We extensively validated the effectiveness and outstanding performance of the algorithm on 17 benchmark functions.