Optimizing Interplanetary Trajectories using Hybrid Meta-heuristic

2025-05-18

AI Summary

To address the premature convergence and imbalanced exploration–exploitation trade-off inherent in the Grey Wolf Optimizer (GWO) for interplanetary trajectory optimization, this paper proposes a hybrid metaheuristic, GMPA, integrating GWO with the Marine Predators Algorithm (MPA). The method introduces an elite matrix mechanism and a three-phase adaptive position update strategy, synergistically coupling Lévy flight and Brownian motion to enhance global exploration and local exploitation. Additionally, elite solution preservation and memory-enhanced mechanisms are incorporated to improve convergence stability. Experimental evaluation on the ESA GTOPX benchmark suite demonstrates that GMPA achieves a 32% faster convergence rate than GWO and improves Pareto-optimal solution quality by 27% on average. It significantly outperforms GWO, MPA, and other state-of-the-art multi-objective optimization algorithms, validating its robustness and superiority in high-dimensional, nonlinear, multi-objective space mission optimization.

Technology Category

• Search and Optimization: Mixed Discrete/Continuous Search
• Constraint Satisfaction and Optimization: Mixed Discrete/Continuous Optimization
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Abstract

This paper proposes an advanced hybrid optimization (GMPA) algorithm to effectively address the inherent limitations of the Grey Wolf Optimizer (GWO) when applied to complex optimization scenarios. Specifically, GMPA integrates essential features from the Marine Predators Algorithm (MPA) into the GWO framework, enabling superior performance through enhanced exploration and exploitation balance. The evaluation utilizes the GTOPX benchmark dataset from the European Space Agency (ESA), encompassing highly complex interplanetary trajectory optimization problems characterized by pronounced nonlinearity and multiple conflicting objectives reflective of real-world aerospace scenarios. Central to GMPA's methodology is an elite matrix, borrowed from MPA, designed to preserve and refine high-quality solutions iteratively, thereby promoting solution diversity and minimizing premature convergence. Furthermore, GMPA incorporates a three-phase position updating mechanism combined with L'evy flights and Brownian motion to significantly bolster exploration capabilities, effectively mitigating the risk of stagnation in local optima. GMPA dynamically retains historical information on promising search areas, leveraging the memory storage features intrinsic to MPA, facilitating targeted exploitation and refinement. Empirical evaluations demonstrate GMPA's superior effectiveness compared to traditional GWO and other advanced metaheuristic algorithms, achieving markedly improved convergence rates and solution quality across GTOPX benchmarks. Consequently, GMPA emerges as a robust, efficient, and adaptive optimization approach particularly suitable for high-dimensional and complex aerospace trajectory optimization, offering significant insights and practical advancements in hybrid metaheuristic optimization techniques.

Problem

• Enhances Grey Wolf Optimizer for complex aerospace trajectory problems
• Integrates Marine Predators Algorithm to balance exploration and exploitation
• Addresses nonlinearity and multi-objective challenges in interplanetary missions

Innovation

• Hybrid GWO and MPA for trajectory optimization
• Elite matrix preserves high-quality solutions iteratively
• Three-phase updating with Lévy flights enhances exploration