A RankNet-Inspired Surrogate-Assisted Hybrid Metaheuristic for Expensive Coverage Optimization

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.

@article{wu2025_2501.07375,
  title={ A RankNet-Inspired Surrogate-Assisted Hybrid Metaheuristic for Expensive Coverage Optimization },
  author={ Tongyu Wu and Changhao Miao and Yuntian Zhang and Fang Deng and Chen Chen },
  journal={arXiv preprint arXiv:2501.07375},
  year={ 2025 }
}