The terminal trajectory optimization of anti-ship missiles presents unique challenges, as these systems must simultaneously complete attitude adjustment and energy management within an extremely constrained timeframe [9]. Compared to traditional ballistic missiles, the optimization of anti-ship missile trajectories involves significantly more complex parameters [10], necessitating the development of advanced optimization methodologies to effectively address the dynamic and unpredictable nature of modern naval combat scenarios. Current trajectory optimization methodologies are primarily categorized into indirect and direct approaches. The indirect method employs optimal control theory to formulate a Hamiltonian boundary value problem, which is subsequently solved using precise numerical integration techniques. This approach, grounded in Pontryagin's Maximum Principle, provides theoretically optimal solutions but is heavily reliant on accurate initial value guesses and struggles to handle complex path constraints effectively [11; 12; 13; 14]. In contrast, the direct optimization approach transforms the trajectory optimization problem into a non-linear programming (NLP) framework. By leveraging various intelligent optimization algorithms, this method seeks to identify the optimal trajectory parameters. While the direct approach has demonstrated a robust performance in complex engineering applications, it is not without limitations. Specifically, the \"curse of dimensionality\" becomes increasingly problematic as the number of time points and constraint variables increase, leading to exponential growth in computational demands [15; 16]. In recent years, remarkable advancements in computer technology have significantly enhanced the performance of evolutionary computation techniques, including genetic algorithms (GA), particle swarm optimization (PSO), and ant colony optimization (ACO), in addressing complex nonlinear trajectory optimization challenges [17; 18; 19; 20; 21; 22; 23]. For instance, [PERSON] et al. proposed a GA-based approach to maximize the glide distance of a hypersonic aircraft [17]. Similarly, Dr. [PERSON] successfully applied GA and its variants to optimize tactical missile trajectories, ensuring the achievement of mission objectives [18]. [PERSON] et al. developed a unified PSO algorithm for the integrated design of long-range missile trajectories, propulsion systems, and aerodynamic parameters [19]. Furthermore, an enhanced ACO algorithm has demonstrated its effectiveness in solving six-degree-of-freedom missile trajectory modeling and optimization problems [20]. [PERSON] et al. introduced a novel framework that combined GA with a twin delayed deep deterministic policy gradient to address trajectory planning for long-range air-to-ground strikes [21]. Compared to conventional optimization methods, these evolutionary algorithms exhibit superior capabilities in achieving global optimization within large-scale search spaces characterized by nonlinear constraints while maintaining strong adaptability and rapid convergence rates [22; 23]. While significant advancements have been made in evolutionary computation methods for typical trajectory optimization problems, their application to the terminal trajectory optimization of anti-ship missiles remains limited. A primary challenge has arisen from the reliance of anti-ship missiles on single-pulse systems or seeker systems to continuously acquire target data during high-speed flight, necessitating trajectory optimization that satisfies the real-time updates required by the guidance loop [24; 25; 26]. Furthermore, during the terminal phase, anti-ship missiles execute multiple large-scale thrust maneuvers based on predefined trigger conditions [27]. Consequently, the optimization process must incorporate constraints related to flight time, motor overload, speed, and altitude [28], which substantially influence the performance of the optimization algorithm.