Sparse span array is a critical communication technology for detecting microwave signal, yet it is difficult to simultaneously satisfy both reducing antenna elements' number and maintaining maximum side lobe level. Towards this problem, we propose a multi-objective optimization approach for self-organizing limited-area sparse span array, termed MOSSA. Overall, a uniform framework of multi-objective sparse span array is proposed. Specially, two objectives, number of selected antenna and peak side lobe level, are established for exploring the optimal array distribution in the framework. Based on the framework, for the problem of global-optimum array distribution, we propose a multi-objective particle swarm optimization searching pattern and design a MOSSA algorithm; Furthermore, for the problem of flexibly-adjusted self-organizing array structure, we present a multi-objective genetic programming searching pattern and design a MOSSA-gp algorithm. Moreover, a limited-region mode supplements to the framework. Finally, combination decision strategy assists users to screen out suitable solutions under the guidance of fuzzy-range indexes and then select the optimal solution by a triangle-approximating approach based on minimum Manhattan distance. Numerous experiments demonstrate that the proposed MOSSA outperforms other state-of-the-art algorithms in terms of both antenna elements’ number and maximum side lobe level.

稀疏跨度阵列是检测微波信号的关键通信技术，但是很难同时满足减少天线元件数量和保持最大旁瓣电平的要求。针对此问题，我们提出了一种用于自组织有限区域稀疏跨度阵列的多目标优化方法，称为MOSSA。总体上，提出了一个多目标稀疏跨度数组的统一框架。特别是，建立了两个目标，即选定天线的数量和峰值旁瓣电平，以探索框架中的最佳阵列分布。在此框架的基础上，针对全局最优阵列分布问题，提出了一种多目标粒子群优化搜索模型，并设计了一种MOSSA算法。此外，针对柔性自组织阵列结构的问题，提出了一种多目标遗传规划搜索模式，并设计了一种MOSSA-gp算法。此外，有限区域模式是对框架的补充。最后，组合决策策略可帮助用户在模糊范围指标的指导下筛选出合适的解决方案，然后基于最小曼哈顿距离，通过三角近似法选择最佳解决方案。许多实验表明，就天线元件的数量和最大旁瓣电平而言，拟议的MOSSA优于其他最新算法。组合决策策略可帮助用户在模糊范围指标的指导下筛选出合适的解决方案，然后通过基于最小曼哈顿距离的三角近似方法选择最佳解决方案。许多实验表明，就天线元件的数量和最大旁瓣电平而言，拟议的MOSSA优于其他最新算法。组合决策策略可帮助用户在模糊范围指标的指导下筛选出合适的解决方案，然后通过基于最小曼哈顿距离的三角近似方法选择最佳解决方案。许多实验表明，就天线元件的数量和最大旁瓣电平而言，拟议的MOSSA优于其他最新算法。