平面陣列方向圖優(yōu)化的改進(jìn)PSO算法
doi: 10.11999/JEIT170167
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2.
(空軍哈爾濱飛行學(xué)院飛行仿真技術(shù)研究所 哈爾濱 150001) ②(哈爾濱工業(yè)大學(xué)導(dǎo)航制導(dǎo)與控制理論中心 哈爾濱 150001)
基金項(xiàng)目:
國家自然科學(xué)基金(61273095)
Modified PSO Algorithm for Planar Arrays Pattern Optimization
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2.
(Flight Simulation Technology Institute, Air Force Harbin Flight Academy, Harbin 150001, China)
Funds:
The National Natural Science Foundation of China (61273095)
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摘要: 粒子群優(yōu)化算法(PSO)是合成天線陣列預(yù)期方向圖的有效手段。但對于某些大型平面陣列方向圖復(fù)雜的非線性優(yōu)化問題，該算法收斂速度慢且最優(yōu)粒子易陷入局部最優(yōu)解，因而使得算法失效。針對這一問題，該文提出一種改進(jìn)PSO算法來提高傳統(tǒng)PSO算法的收斂特性。該算法在初始化最優(yōu)粒子時(shí)采用解析初值而不是隨機(jī)初值。對于給定的預(yù)期方向圖，通過矩陣運(yùn)算解析對應(yīng)該方向圖的陣元權(quán)系數(shù)。之后將這些權(quán)系數(shù)指定為任意一個(gè)粒子的解析初值，而種群的其他粒子仍然賦隨機(jī)初值，之后再銜接標(biāo)準(zhǔn)PSO算法的尋優(yōu)迭代過程。這種初始化方法使得種群粒子在尋優(yōu)搜索過程開始之前，即可得到最優(yōu)粒子初值的有效估計(jì)。仿真結(jié)果表明，相對于全部粒子賦隨機(jī)初值的標(biāo)準(zhǔn)算法而言，這種改進(jìn)算法收斂速度更快，適應(yīng)度值收斂得更深，因而有效提高了算法的收斂特性，從而能夠得到滿足預(yù)期方向圖指標(biāo)要求的優(yōu)化結(jié)果。
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關(guān)鍵詞:
- 有源相控陣 /
- 平面陣列 /
- 副瓣干擾 /
- 方向圖優(yōu)化 /
- 粒子群優(yōu)化算法
Abstract: Particle Swarm Optimization (PSO) is an efficient technology to synthesize desired pattern of antenna arrays. But in some complicated cases, the optimizer will fail because the optimum particles fall into local best solutions and the convergence is so bad, especially for nonlinear optimization of large planar arrays pattern. To solve this problem, a modified optimizer is presented to improve the convergence of traditional PSO by means of initializing the particles with analytical values rather than random values. For any given desired pattern, the corresponding aperture weights can be derived by matrix operations and these weights are then used as a particles initial values while other particles are still initialized randomly. By this initialization, an efficient estimation of the optimum particles initial values can be achieved before the beginning of all particles searching process. After that the standard PSO iterations work as usual. The simulation results prove that this modified optimizer converges more rapidly and deeply than the traditional PSO and more satisfying global solutions and desired pattern could be obtained. -
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