发表机构
School of Mathematics and Computational Science, Xiangtan University（湘潭大学数学与计算科学学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出一种基于Levy长跳与水平-垂直交叉的混合交叉袋鼠逃逸优化框架，在FEALPy多后端实现，经CEC2022测试和工程问题验证，并成功应用于无人机路径规划。
AI 中文摘要
复杂的工程优化问题通常具有多模态、高维度和非线性约束的特点，这给高效可靠的计算带来了重大挑战。为了解决这些挑战，本文开发了一种基于混合交叉的优化框架，该框架增强了种群交互并提高了搜索效率。所提出的框架整合了两种互补机制，即用于全局探索的Levy长跳交叉策略和用于有效信息交换与局部细化的水平-垂直交叉策略，从而改善了收敛行为和鲁棒性。为了支持可扩展计算，该方法在基于FEALPy的统一多后端计算框架内实现，使得在异构平台上（包括CPU和GPU上的NumPy和PyTorch）能够一致且高效地执行。这种设计增强了大规模优化任务中的可移植性、可复现性和计算效率。在IEEE CEC2022基准测试套件上的大量实验表明，与几种代表性元启发式算法相比，所提出的框架取得了具有竞争力或更优的性能，并通过Wilcoxon秩和检验和Friedman统计检验进行了验证。此外，该方法在约束工程设计问题上表现出强大的性能。最后，将所提出的框架应用于无人机路径规划，该问题被表述为约束优化问题，展示了其在复杂工程场景中的有效性和可扩展性。
英文摘要
Complex engineering optimization problems are often characterized by multimodality, high dimensionality, and nonlinear constraints, posing significant challenges for efficient and reliable computation. To address these challenges, this paper develops a hybrid crossover-based optimization framework that enhances population interaction and improves search efficiency. The proposed framework integrates two complementary mechanisms, namely a Levy long jump crossover strategy for global exploration and a horizontal-vertical crossover strategy for effective information exchange and local refinement, thereby improving convergence behavior and robustness. To support scalable computation, the method is implemented within a unified multi-backend computational framework based on FEALPy, enabling consistent and efficient execution across heterogeneous platforms, including NumPy and PyTorch on both CPU and GPU. This design enhances portability, reproducibility, and computational efficiency in large-scale optimization tasks. Extensive experiments on the IEEE CEC2022 benchmark suite demonstrate that the proposed framework achieves competitive or superior performance compared with several representative metaheuristic algorithms, as validated by Wilcoxon rank-sum and Friedman statistical tests. In addition, the method shows strong performance on constrained engineering design problems. Finally, the proposed framework is applied to UAV path planning, formulated as a constrained optimization problem, demonstrating its effectiveness and scalability in complex engineering scenarios.
Comments10 pages, 63 figures
