An Adaptive Population Size Evolutionary Algorithm for the Sensor Deployment Problem

With the rapid development of the Internet of Things and smart cities, effectively deploying sensor networks has become crucial for enhancing monitoring and control efficiency. This paper introduces an evolutionary algorithm that utilizes a dynamic population size to optimize sensor deployment strategies in specific areas. The algorithm adapts the population size to balance the breadth and depth of the search space, thereby improving convergence speed and solution quality in complex environments. We tested the algorithm across various problems of different scales, and the results indicate that our method outperforms conventional evolutionary algorithms in both solution quality and efficiency. This demonstrates that our proposed approach offers an effective solution for optimizing sensor network deployment and holds significant potential for broader applications.