【摘要】：現(xiàn)實生活中最優(yōu)化問題普遍存在,且往往涉及相互矛盾的多個目標(biāo)。由于多項設(shè)計指標(biāo)的引入,導(dǎo)致問題的搜索空間明顯擴大,求解難度激增,因此,為求解計算量較大的多目標(biāo)優(yōu)化問題,充分利用智能優(yōu)化算法的尋優(yōu)能力和大規(guī)模高性能并行計算技術(shù)是切實可行的解決方案。本文基于非支配排序團隊進步算法(NRTPA),引入自適應(yīng)思想和并行化方案,提出了并行多目標(biāo)團隊進步算法。并行多目標(biāo)團隊進步算法將雙群體演化機制和非支配排序的多目標(biāo)策略相結(jié)合,以高效的尋優(yōu)效率提升多目標(biāo)解集的逼近性、均勻性和寬廣性。根據(jù)測試函數(shù)集的直觀驗證和度量指標(biāo)的定量對比,測試結(jié)果表明,并行多目標(biāo)團隊進步算法具備快速的收斂能力和良好的解集分布性,同時,算法穩(wěn)定性也明顯提高。選擇直線陣為優(yōu)化模型,將并行多目標(biāo)算法應(yīng)用于天線陣方向圖的優(yōu)化設(shè)計,對各陣元的激勵幅值進行優(yōu)化,以降低旁瓣電平和設(shè)計零陷位置。詳細介紹了目標(biāo)函數(shù)的建立過程,利用天線陣的對稱結(jié)構(gòu)和輻射特點,有效地縮減了可行域的搜索空間。在天線陣的應(yīng)用算例中,算法能找到一系列優(yōu)秀解,且在多個優(yōu)化目標(biāo)上表現(xiàn)良好,表明并行多目標(biāo)團隊進步算法具備解決電磁場優(yōu)化等實際工程問題的能力。
[Abstract]:In real life, optimization problems are common and often involve conflicting goals. Because of the introduction of many design indexes, the search space of the problem is obviously enlarged, and the difficulty of solving the problem is greatly increased. Therefore, in order to solve the multi-objective optimization problem, which has a large amount of computation, It is a feasible solution to make full use of the optimization ability of intelligent optimization algorithm and large scale high performance parallel computing technology. This paper presents a parallel multi-objective team progress algorithm based on the adaptive idea and parallelization scheme based on the non-dominated ranking team progress algorithm (NRTPA),). The parallel multi-objective team progress algorithm combines the two-population evolution mechanism with the non-dominated sorting multi-objective strategy to improve the approximation uniformity and broadness of the multi-objective solution set with efficient optimization efficiency. According to the visual verification of test function set and the quantitative comparison of measurement index, the test results show that the parallel multi-objective team progress algorithm has fast convergence ability and good solution set distribution, and the stability of the algorithm is improved obviously. The parallel multi-objective algorithm is applied to the optimization design of antenna array pattern, and the excitation amplitude of each array element is optimized to reduce the sidelobe level and design zero trapping position. The establishment process of objective function is introduced in detail. The search space of feasible region is reduced effectively by using the symmetrical structure and radiation characteristics of antenna array. In the application example of antenna array, the algorithm can find a series of excellent solutions and perform well on multiple optimization objectives, which shows that the parallel multi-objective team progress algorithm has the ability to solve practical engineering problems such as electromagnetic field optimization.
【學(xué)位授予單位】：南京郵電大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TP18

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本文編號：2231463