基于改进差分进化算法的簇馈源波束合成优化
首发时间:2021-04-15
摘要:簇馈源次级波束合成优化是一个复杂的非线性多峰函数的优化问题,而差分进化(Differential Evolution,DE)是一种基于种群成员之间的差异来进行突变的随机优化方法。在本文中,利用带有邻域搜索的自适应差分进化(Self-adaptive Differential Evolution with Neighborhood Search,SaNSDE)算法求解波束合成优化问题。在此基础上,基于种群个体在实际场景中不同的搜索区间,提出了改进型的带有邻域搜索的自适应差分进化(Modified Self- adaptive Differential Evolution with Neighborhood Search,MSaNSDE)算法。将所提算法与综合学习粒子群(Comprehensive Learning Particle Swarm,CLPSO)算法、SaNSDE算法进行比较,仿真结果验证了所提算法的有效性。
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Cluster-feed beam synthesis optimization based on improved differential evolution algorithm
Abstract:Cluster-feed secondary beam synthesis optimization is a complex nonlinear multimodal function optimization problem, and Differential Evolution (DE) is a random optimization method based on the differences between population members to perform mutations. In this paper, the self-adaptive differential evolution (Self-adaptive Differential Evolution with Neighborhood Search, SaNSDE) algorithm is used to solve the beam synthesis optimization problem. On this basis, based on the different search intervals of the population individuals in the actual scene, an improved Modified Self-adaptive Differential Evolution with Neighborhood Search (MSaNSDE) algorithm is proposed. The proposed algorithm is compared with the Comprehensive Learning Particle Swarm (CLPSO) algorithm and the SaNSDE algorithm. The simulation results verify the effectiveness of the proposed algorithm.
Keywords: Beam synthesis optimization differential evolution algorithm neighborhood search self- adaptive
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基于改进差分进化算法的簇馈源波束合成优化
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