Synthesis of MIMO Radar Sparse Arrays Using a Hybrid Improved Fireworks-Simulated Annealing Algorithm

This study proposes a hybrid optimization algorithm (IFWA-SA) integrating an improved fireworks algorithm with simulated annealing for sparse array synthesis in multiple-input multiple-output (MIMO) radar systems. The innovation lies in synergistically combining the multidimensional directional explosion mechanism of the fireworks algorithm for global exploration with simulated annealing’s probabilistic jumping strategy for local optimization. Initial populations generated via Sobol sequences eliminate local clustering from random initialization. During global exploration, the proposed discrete variant of the fireworks algorithm, tailored for sparse array optimization, significantly enhances the search efficiency, while temperature-controlled probabilistic optimization refines array aperture and element spacing to escape local optima during local refinement. Comparative experiments with particle swarm optimization (PSO), simulated annealing (SA), genetic algorithm (GA) and gray wolf optimization (GWO) demonstrated that the proposed method effectively suppresses sidelobes. On average, the IFWA-SA reduced the peak sidelobe level (PSL) by about 1.3–3.8 dB compared with the benchmark algorithms, confirming its superior convergence capability and effectiveness in synthesizing high-performance sparse arrays.