An effective population-based approach for the partial set covering problem

Ye Zhang, Jinlong He, Yupeng Zhou, Shuli Hu, Dunbo Cai, Naiyu Tian, Minghao Yin

Abstract: The partial set covering problem (PSCP) is a significant combinatorial optimization problem that finds applications in numerous real-world scenarios. The objective of PSCP is to encompass a minimum number of subsets while ensuring the coverage of at least n elements. Due to its NP-hard nature, solving large-scale PSCP efficiently remains a critical issue in computational intelligence. To effectively tackle this challenge, we delve into a population-based approach that incorporates a modified tabu search, thereby striking a delicate balance between exploration and exploitation. To further enhance its efficacy, we employ the multiple path-relinking strategy and the fix-and-optimize process. Finally, the dynamic resource allocation scheme is utilized to save computing efforts. Comparative experiments of the proposed algorithm were conducted against three state-of-the-art competitors, across two distinct categories, encompassing 150 instances. The results significantly underscore the profound effectiveness of our proposed algorithm, as evidenced by the updating of 67 best-known solutions. Moreover, we conduct an in-depth analysis of the key components inherent to the algorithm, shedding light on their respective influences on the whole performance.