摘要
为提高天线阵列方向性图综合优化计算效率,提出了一种新的双群体演化算法。该算法将优化变量向量模拟为团队成员,把该团队分为精英组和普通组,建立了两组的学习样板,定义了学习和探索运算,并合理设定了成员更新规则。两组成员在搜索过程中出现了明显分工,使算法兼备了全局搜索、局部搜索和定向搜索的能力。数值结果验证了新算法具有实现简单、计算量少和参数选择相对容易等特性。给出了用该算法对天线阵幅度、相位和幅相综合的数值结果,验证了新算法的有效性。该算法对解决同类的优化问题具有应用价值。
A novel evolutionary algorithm is presented in order to improve the efficiency of pattern synthesis of antenna arrays. In the algorithm, the optimized variable vectors are simulated as members of a team. After the team members are divided into the elite and plain groups, the epitomes of both groups are determined, the manipulations of learning and exploration are properly defined, and the member renewal rules are reasonably established. The members' division of action can emerge during the search procedure, which makes the algorithm possesses the abilities of global, local and directional search. Numerical results verify that the new algorithm has the properties of simple implementation, fast convergence, low computation cost and easy parameter selection. Examples of amplitude-only, phase-only and amplitude-phase cxeitations of antenna arrays are given. The algorithm can be employed to solve similar optimization problems.
出处
《微波学报》
CSCD
北大核心
2007年第5期1-6,共6页
Journal of Microwaves
基金
国家自然科学基金项目(60471046)
关键词
演化算法
全局优化
方向图综合
天线阵列
Evolutionary algorithm, Global optimization, Pattern synthesis, Antenna array