一种基于RankNet的代理辅助混合元启发式算法用于昂贵覆盖优化
A RankNet-Inspired Surrogate-Assisted Hybrid Metaheuristic for Expensive Coverage Optimization
AI总结：
针对昂贵混合变量覆盖优化问题，提出基于RankNet的代理辅助混合元启发式算法RI-SHM，通过成对排名代理、局部分布估计和动态切换策略，高效处理大规模任务，性能最高提升56.5%。
摘要：
覆盖优化通常涉及部署一组设施以最佳地满足指定点的需求，在选址科学和传感器网络等领域有着广泛的应用。最近的应用表明，子集选址与连续角度参数优化相结合可以表述为混合变量优化问题（MVOPs）。同时，高保真离散化和可见性分析显著增加了计算成本和复杂度，将MVOP演变为昂贵的混合变量优化问题（EMVOP）。虽然经典进化算法已取得有前景的结果，但它们对大量适应度评估的依赖对我们的问题而言代价过高。此外，大多数代理辅助方法因依赖基于回归的模型而面临局限性。为解决这些问题，我们提出了基于RankNet的代理辅助混合元启发式算法（RI-SHM），这是我们先前工作的扩展。RI-SHM整合了三个关键组件：（1）一个基于RankNet的成对全局代理，创新性地预测个体对之间的排名，绕过了不连续解空间中适应度估计的挑战；（2）一个代理辅助的局部分布估计算法，增强局部开发能力并帮助逃离局部最优；（3）一个适应度多样性驱动的切换策略，动态平衡探索与开发。实验表明，我们的算法能够在理想的运行时间内有效处理高达300维和超过1800个目标的大规模覆盖优化任务。与最先进的EMVOP算法相比，RI-SHM在所有测试实例上始终优于它们，最高提升达56.5%。
英文摘要：
Coverage optimization generally involves deploying a set of facilities to best satisfy the demands of specified points, with broad applications in fields such as location science and sensor networks. Recent applications reveal that the subset site selection coupled with continuous angular parameter optimization can be formulated as Mixed-Variable Optimization Problems (MVOPs). Meanwhile, high-fidelity discretization and visibility analysis significantly increase computational cost and complexity, evolving the MVOP into an Expensive Mixed-Variable Optimization Problem (EMVOP). While canonical Evolutionary Algorithms have yielded promising results, their reliance on numerous fitness evaluations is too costly for our problem. Furthermore, most surrogate-assisted methods face limitations due to their reliance on regression-based models. To address these issues, we propose the RankNet-Inspired Surrogate-assisted Hybrid Metaheuristic (RI-SHM), an extension of our previous work. RI-SHM integrates three key components: (1) a RankNet-based pairwise global surrogate that innovatively predicts rankings between pairs of individuals, bypassing the challenges of fitness estimation in discontinuous solution space; (2) a surrogate-assisted local Estimation of Distribution Algorithm that enhances local exploitation and helps escape from local optima; and (3) a fitness diversity-driven switching strategy that dynamically balances exploration and exploitation. Experiments demonstrate that our algorithm can effectively handle large-scale coverage optimization tasks of up to 300 dimensions and more than 1,800 targets within desirable runtime. Compared to state-of-the-art algorithms for EMVOPs, RI-SHM consistently outperforms them by up to 56.5$\%$ across all tested instances.
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