粒子群优化算法在天线设计.doc
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粒子群优化算法在天线设计,摘要粒子群优化算法(particle swarm optimization，pso)是近十几年新出现的一种基于群迭代的模拟群体生物相互协同寻优的启发式优化算法，因其收敛速度快和易于实现等特点，已经成为计算智能领域新的研究热点。自2002年j. robinson首次使用pso算法设计了赋形波纹喇叭天线，此后，粒子群优化算...
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摘 要
粒子群优化算法(Particle Swarm Optimization，PSO)是近十几年新出现的一种基于群迭代的模拟群体生物相互协同寻优的启发式优化算法，因其收敛速度快和易于实现等特点，已经成为计算智能领域新的研究热点。自2002年J. Robinson首次使用PSO算法设计了赋形波纹喇叭天线，此后，粒子群优化算法在天线设计，特别是阵列天线设计问题中得到了大量广泛的应用。由于粒子群算法理论基础还不完备，并存在早熟收敛的问题，因此，对算法进行深入的理论分析及改进是研究者们的工作重点之一。基于这种情况，本文对标准粒子群算法的边界条件进行了分析，为了提高算法的全局搜索能力及收敛速度分别提出了二进制粒子群（Binary Particle Swarm Optimization，BPSO）和量子粒子群（Quantum Particle Swarm Optimization，QPSO）的改进算法，并将改进后的算法用于天线阵列综合问题。本文主要研究内容如下：
1. 研究了标准粒子群算法的边界条件设置，基于前人研究的基础，提出了一组新的受限制的边界条件，即，将出界粒子随机置于搜索空间内。除此之外，还将吸收的优点引入到现有的无形边界条件中，组合成无形/吸收的边界条件。由仿真结果对比分析，新提出的随机重置的边界条件的性能明显优于置于边界的情况，无形/吸收的边界条件也稍微优于其他不受限制的边界条件。
2. 为了提高算法的收敛速度和全局搜索能力，本文提出了两种二进制粒子群的改进算法，分别通过引入全局次优活跃点和鲶鱼扰动机制，并对改进算法进行参数设置，通过典型测试函数验证，改进算法和参数设置方法的可行性和有效性。
3. 研究了Sun等人提出的量子粒子群算法，为了克服算法早熟收敛，从收敛速度和收敛精度两个方面提出了随机搜索和反向学习机制两种改进思路，综合两种改进方法，提出多种组合的QPSO改进算法，通过测试函数验证，改进算法加快了算法收敛速度并提高了算法的收敛精度。
4. 利用改进的量子粒子群算法综合直线阵列，给出具体通信要求的天线阵实例，综合结果优于现有文献结果。
5. 研究了改进算法综合平面阵，包括矩形面阵和圆阵的阵元幅度优化和稀疏阵问题，讨论了阵元数目、圆环半径等参数的选取对单层圆环和多层同心圆环的方向图的影响。
6. 最后研究了共形阵综合，同样分析阵元行间距、圆环半径等参数对阵列方向图的影响，并研究适应度函数的选取对方向图的影响，最后简单研究了圆锥阵的综合。
关键字 粒子群算法，二进制粒子群，量子粒子群，天线阵，方向图综合
Abstract
Particle Swarm Optimization (PSO) algorithm is a kind of heuristic optimization algorithm based on swarm iteration. It has become a new hotspot in the area of computation intelligence for the rapid convergence and simple implementation. Since it is recently introduced to design the corrugated horn antenna first by Robinson and Rahmat-Samii in 2002, after that, PSO algorithm get a lot of wide used in antenna design, especially antenna array design. Due to the theoretical basic of PSO algorithm is not complete, and it also has a problem of premature convergence, deep theoretical analysis and improvements of algorithm is one of the most important work of researchers. Based on this situation, the boundary conditions of standard PSO are analyzed in order to improve the global search ability and the algorithm convergence speed, the improvements of Binary Particle Swarm Optimization (BPSO) and Quantum Particle Swarm Optimization (QPSO) are presented, and the improved algorithms are used in antenna array synthesis. The main research works are discussed as follows:
1. The set of standard PSO boundary conditions are studied. Based on the basis of previous studies, a group of new boundary conditions are put forward, namely, errant particles are relocated in the solution space randomly. In addition, an invisible/absorbing boundary condition is proposed in which the favorable characteristic of the absorbing boundary condition is introduced into the existing invisible boundary condition. The simulation results show that the new boundary conditions which relocate errant particles randomly offer superior performance than relocate errant particles on the boundary of solution space. The invisible/absorbing boundary condition edges out other unrestricted boundary conditions.
2. In order to improve the algorithm convergence speed and the global search ability, two kinds of improved BPSOs are put forward, respectively. By introducing global subprime active point and catfish perturbation mechanism, the parameters set of improved algorithms are researched. Through the typical functions’ test and verify, the improved algorithms and the methods of parameters setting are feasible and effective.
3. QPSO algorithm proposed by Sun and others is studied. In order to overcome algorithm premature convergence, random search and reverse learning mechanism are put forward separately in convergence speed and the accuracy of convergence. Considering these two improvement ideas comprehensively, several combination improved algorithms are suggested. The simulation results of test functions show that the improved algorithms can speed up the convergence rate and improve the algorithm convergence precision.
4. The improved QPSO is used to synthesis linear array, the examples of specific communication requirements are given, its results are better than the existing literatures’ results.
5. The plane array synthesis using improved algorit..
粒子群优化算法(Particle Swarm Optimization，PSO)是近十几年新出现的一种基于群迭代的模拟群体生物相互协同寻优的启发式优化算法，因其收敛速度快和易于实现等特点，已经成为计算智能领域新的研究热点。自2002年J. Robinson首次使用PSO算法设计了赋形波纹喇叭天线，此后，粒子群优化算法在天线设计，特别是阵列天线设计问题中得到了大量广泛的应用。由于粒子群算法理论基础还不完备，并存在早熟收敛的问题，因此，对算法进行深入的理论分析及改进是研究者们的工作重点之一。基于这种情况，本文对标准粒子群算法的边界条件进行了分析，为了提高算法的全局搜索能力及收敛速度分别提出了二进制粒子群（Binary Particle Swarm Optimization，BPSO）和量子粒子群（Quantum Particle Swarm Optimization，QPSO）的改进算法，并将改进后的算法用于天线阵列综合问题。本文主要研究内容如下：
1. 研究了标准粒子群算法的边界条件设置，基于前人研究的基础，提出了一组新的受限制的边界条件，即，将出界粒子随机置于搜索空间内。除此之外，还将吸收的优点引入到现有的无形边界条件中，组合成无形/吸收的边界条件。由仿真结果对比分析，新提出的随机重置的边界条件的性能明显优于置于边界的情况，无形/吸收的边界条件也稍微优于其他不受限制的边界条件。
2. 为了提高算法的收敛速度和全局搜索能力，本文提出了两种二进制粒子群的改进算法，分别通过引入全局次优活跃点和鲶鱼扰动机制，并对改进算法进行参数设置，通过典型测试函数验证，改进算法和参数设置方法的可行性和有效性。
3. 研究了Sun等人提出的量子粒子群算法，为了克服算法早熟收敛，从收敛速度和收敛精度两个方面提出了随机搜索和反向学习机制两种改进思路，综合两种改进方法，提出多种组合的QPSO改进算法，通过测试函数验证，改进算法加快了算法收敛速度并提高了算法的收敛精度。
4. 利用改进的量子粒子群算法综合直线阵列，给出具体通信要求的天线阵实例，综合结果优于现有文献结果。
5. 研究了改进算法综合平面阵，包括矩形面阵和圆阵的阵元幅度优化和稀疏阵问题，讨论了阵元数目、圆环半径等参数的选取对单层圆环和多层同心圆环的方向图的影响。
6. 最后研究了共形阵综合，同样分析阵元行间距、圆环半径等参数对阵列方向图的影响，并研究适应度函数的选取对方向图的影响，最后简单研究了圆锥阵的综合。
关键字 粒子群算法，二进制粒子群，量子粒子群，天线阵，方向图综合
Abstract
Particle Swarm Optimization (PSO) algorithm is a kind of heuristic optimization algorithm based on swarm iteration. It has become a new hotspot in the area of computation intelligence for the rapid convergence and simple implementation. Since it is recently introduced to design the corrugated horn antenna first by Robinson and Rahmat-Samii in 2002, after that, PSO algorithm get a lot of wide used in antenna design, especially antenna array design. Due to the theoretical basic of PSO algorithm is not complete, and it also has a problem of premature convergence, deep theoretical analysis and improvements of algorithm is one of the most important work of researchers. Based on this situation, the boundary conditions of standard PSO are analyzed in order to improve the global search ability and the algorithm convergence speed, the improvements of Binary Particle Swarm Optimization (BPSO) and Quantum Particle Swarm Optimization (QPSO) are presented, and the improved algorithms are used in antenna array synthesis. The main research works are discussed as follows:
1. The set of standard PSO boundary conditions are studied. Based on the basis of previous studies, a group of new boundary conditions are put forward, namely, errant particles are relocated in the solution space randomly. In addition, an invisible/absorbing boundary condition is proposed in which the favorable characteristic of the absorbing boundary condition is introduced into the existing invisible boundary condition. The simulation results show that the new boundary conditions which relocate errant particles randomly offer superior performance than relocate errant particles on the boundary of solution space. The invisible/absorbing boundary condition edges out other unrestricted boundary conditions.
2. In order to improve the algorithm convergence speed and the global search ability, two kinds of improved BPSOs are put forward, respectively. By introducing global subprime active point and catfish perturbation mechanism, the parameters set of improved algorithms are researched. Through the typical functions’ test and verify, the improved algorithms and the methods of parameters setting are feasible and effective.
3. QPSO algorithm proposed by Sun and others is studied. In order to overcome algorithm premature convergence, random search and reverse learning mechanism are put forward separately in convergence speed and the accuracy of convergence. Considering these two improvement ideas comprehensively, several combination improved algorithms are suggested. The simulation results of test functions show that the improved algorithms can speed up the convergence rate and improve the algorithm convergence precision.
4. The improved QPSO is used to synthesis linear array, the examples of specific communication requirements are given, its results are better than the existing literatures’ results.
5. The plane array synthesis using improved algorit..
