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  4. Vol 10, Issue 1, 2026

Improved Genetic Algorithm with Dynamic Set Cover Modeling for Coordinated Spatiotemporal Coverage Scheduling

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DOI: 10.23977/acss.2026.100119 | Downloads: 9 | Views: 104

Author(s)

Zhangwei Xu 1, Yutong Jia 2, Chenghao Zhang 3, Zhen Wang 1

Affiliation(s)

1 School of Mechanical Engineering, Guangxi University, Nanning, China
2 School of Mathematics and Information Sciences, Guangxi University, Nanning, China
3 School of Computer, Electronics and Information, Guangxi University, Nanning, China

Corresponding Author

Zhen Wang

ABSTRACT

Coordinated deployment of autonomous agents for spatiotemporal coverage of moving targets poses a fundamentally combinatorial optimization challenge that couples three-dimensional geometric reasoning, temporal sequencing, and multi-target resource allocation. This paper presents a hybrid evolutionary framework that integrates an improved genetic algorithm with K-Means spatial clustering and a dynamic set cover formulation to schedule coordinated coverage actions executed by autonomous agents against multiple high-velocity trajectories. The proposed architecture discretizes each protected volume into a structured lattice of sixty horizontal and thirty vertical line-of-sight samples, and reformulates effective coverage as the temporal union of geometric intersection events along the target path. An improved genetic algorithm with adaptive crossover and mutation operators inherited from particle swarm dynamics jointly optimizes agent heading, velocity, release coordinates, and detonation timing across an eight-dimensional decision space. Experimental evaluation across five progressively complex scenarios demonstrates that single-agent optimization achieves a coverage interval of 4.65 seconds, representing a 237% improvement over the geometric baseline of 1.38 seconds. Three-agent coordination reaches 13.15 seconds, while the five-agent multi-target dynamic set cover configuration attains 28.60 seconds with 72% reduction in redundant overlap. The framework provides an interpretable and scalable paradigm for cooperative coverage scheduling in autonomous multi-agent systems operating under temporal and geometric constraints.

KEYWORDS

Improved Genetic Algorithm, Dynamic Set Cover Optimization, K-Means Spatial Clustering, Particle Swarm Hybridization, Three-Dimensional Coverage Scheduling, Coordinated Agent Deployment

CITE THIS PAPER

Zhangwei Xu, Yutong Jia, Chenghao Zhang, Zhen Wang. Improved Genetic Algorithm with Dynamic Set Cover Modeling for Coordinated Spatiotemporal Coverage Scheduling. Advances in Computer, Signals and Systems (2026). Vol. 10, No. 1, 159-169. DOI: http://dx.doi.org/10.23977/acss.2026.100119.

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