A matrix-assisted surrogate particle swarm optimization algorithm for multi-objective deployment of solar insecticidal lamps

Solar Insecticidal Lamps (SILs) are a green and efficient pest control method in modern agriculture, but their deployment must balance coverage efficiency, cost, and uniformity, especially in irregular and partitioned farmland. To address this challenge, this paper formulates the Solar Insecticidal Lamp Deployment Problem (SILDP) as a multi-objective optimization problem and proposes a Matrix-based Assisted Surrogate-Aided Multi-Objective Particle Swarm Optimization (MASA-MOPSO) algorithm. This method incorporates matrix computation to fully exploit the parallel computing capabilities of modern platforms. The Spatially-aware Crowding Distance (SCD) calculation is adopted to update the external archive and select leader particles, enhancing the diversity and uniformity of the solution set. During the particle swarm evolution process, a surrogate-guided mechanism is introduced to reduce the computational burden of the objective functions and assist in exploring potentially high-quality regions, thereby enhancing local search capabilities. Meanwhile, a new calculation method for smoothed coverage probability is introduced to address the hard-threshold issues in traditional coverage probability metrics. This improvement allows the incorporation of gradient information to adaptively adjust particle step sizes, thereby accelerating convergence and enhancing optimization accuracy. Experiments in complex farmland environments show that MASA-MOPSO significantly outperforms eight advanced algorithms in terms of deployment cost, coverage, overlap, variability, and runtime, demonstrating its strong effectiveness in solving the SILDP.