Abstract: Coverage rate and cost are two important measures of a video surveillance network for high core rockfill dams, but previous studies in the literature lack a comprehensive consideration of deployment cost, and the commonly-used optimization methods have shortcomings such as slow convergence and easy falling into local optimization. To solve these problems, this paper develops a nonlinear chaotic Harris hawks optimization model for large-scene video monitoring network coverage of a high core rockfill dam. First, a resampling rate per camera is employed to represent deployment cost, and an optimization model is constructed to maximize the coverage rate and resampling rate per camera based on the set covering theory. Then, a chaotic sequence and the nonlinear energy update strategy are adopted to optimize the population initialization and search process of the Harris hawks algorithm, which improves the convergence and avoids falling into prematurity. Finally, an improved algorithm is used to solve this new model. Application to a dam construction project has verified our improved Harris hawks algorithm is effective and superior in the deployment optimization of the surveillance network, achieving a coverage rate and resampling points proportion of up to 99.98% and 60.3% respectively, or 13.8% and 23.2% higher than the empirical scheme, with the video surveillance effect improved significantly.

Key words: high core rockfill dam, video surveillance network coverage, improved Harris hawks optimization algorithm, set covering problem, resampling rate per camera