A radar deployment optimization method based on multileader mutation particle swarm optimization

In shooting range measurement and control experiments, a critical objective is to optimize the deployment of measurement and control equipment. This paper introduces a multi-objective fitness function that integrates essential metrics such as track coverage rate and redundancy, essential for achieving comprehensive experimental goals. We present a distance- and velocity-based Multi-leader Mutation Particle Swarm Optimization (MMPSO) approach, designed to enhance particle convergence toward multiple local optima in the preliminary stages, subsequently accelerating the initial search process. To overcome the challenges associated with the application of variable inertia weights, an adaptive inertia strategy is incorporated, improving local search effectiveness. Furthermore, a probabilistic mutation factor, tailored to the specific requirements of radar deployment, is integrated to select the most favorable solution among various local optima, thus enhancing the algorithm's search speed. Simulation studies comparing the proposed method with several prevalent intelligent optimization algorithms for radar deployment indicate an approximate 30% improvement in convergence speed relative to Simulated Annealing Particle Swarm Optimization (SAPSO), thereby confirming the effectiveness and practical applicability of the proposed radar deployment model.