Integrating particle swarm optimization (PSO) and binary PSO into multiobjective evolutionary algorithm based on decomposition (MOEA/D) by group operator in parallel, an improved mixed optimization algorithm MOEA/D-M is proposed as an automation design scheme for compact high-isolation multiple-input–multiple-output (MIMO) antenna design. When the algorithm runs, each particle has several neighboring particles and all the particles are subdivided into a few groups. Both groups and neighborhoods provide helpful or potential information to their members. Then, under predefined constraints, MIMO antenna with anticipated performance can be generated intelligently and targetedly. The effectiveness of this design scheme is demonstrated by a single-band and a dual-band compact high-isolation MIMO antenna design for wireless local area network (WLAN)/world interoperability for microwave access (WiMAX) applications sharing the same initial reference model. The design degree of freedom of MIMO antennas may be increased greatly with the proposed design technique.

中文翻译：

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基于多目标混合优化算法的紧凑型高隔离度MIMO天线设计

将粒子群优化（PSO）和二元粒子群优化算法并行集成到基于分解的多目标进化算法（MOEA/D）中，提出一种改进的混合优化算法MOEA/DM作为紧凑型高隔离多目标的自动化设计方案。输入多输出 (MIMO) 天线设计。算法运行时，每个粒子都有几个相邻的粒子，所有的粒子又被细分为几组。团体和社区都向其成员提供有用或潜在的信息。然后，在预定义的约束下，可以智能地、有针对性地生成具有预期性能的 MIMO 天线。该设计方案的有效性通过用于共享相同初始参考模型的无线局域网 (WLAN)/微波接入世界互操作性 (WiMAX) 应用的单频段和双频段紧凑型高隔离度 MIMO 天线设计得到证明。提出的设计技术可以大大提高MIMO天线的设计自由度。