本文選題：多層系統(tǒng) + 多目標優(yōu)化�。� 參考：《計算機集成制造系統(tǒng)》2017年11期

【摘要】：針對多層系統(tǒng)多目標優(yōu)化的多艙段衛(wèi)星設(shè)備布局設(shè)計的工程滿意Pareto最優(yōu)解集求解困難問題,基于協(xié)同進化框架,給出一種人機結(jié)合的多目標協(xié)同差異進化算法。算法將人機結(jié)合方法引入基于協(xié)同進化框架的多目標協(xié)同進化算法,構(gòu)建含人工和算法的精英解集合的檔案,提出用差異進化算法隨機自動實現(xiàn)由算法子個體和人工子個體兩兩組合生成個體的3種方式,用于子系統(tǒng)的人機結(jié)合。在迭代前期采用子系統(tǒng)人機結(jié)合增加算法的多樣性和分散性,在迭代后期采用系統(tǒng)人機結(jié)合增加算法的收斂性和分布性,從而提高基于協(xié)同進化框架的多目標協(xié)同進化算法求解這類多層系統(tǒng)的多目標優(yōu)化的計算性能,獲得Pareto最優(yōu)解集。最后,通過案例驗證了該算法具有較好的計算性能。
[Abstract]:In order to solve the problem of engineering satisfactory Pareto optimal solution set for multi-level system multi-objective optimization satellite equipment layout design, a multi-objective co-differential evolution algorithm based on co-evolution framework is proposed. The man-machine combination method is introduced into the multi-objective co-evolution algorithm based on co-evolution framework, and the file of elite solution set with artificial and algorithm is constructed. In this paper, three methods of generating individuals by the combination of algorithm sub-individuals and artificial sub-individuals are proposed, which are used in man-machine combination of subsystems. In the early stage of iteration, the diversity and dispersion of the algorithm are increased by using the man-machine combination of subsystems, and the convergence and distribution of the algorithm are increased by the combination of system and man-machine in the later stage of iteration. Therefore, the multi-objective co-evolution algorithm based on co-evolution framework can improve the computational performance of solving multi-objective optimization for this kind of multi-layered system, and obtain the Pareto optimal solution set. Finally, a case study shows that the algorithm has good computational performance.
【作者單位】： 大連理工大學機械工程學院;大連理工大學計算機科學與技術(shù)學院;
【基金】：國家自然科學基金資助項目(61472062)~~
【分類號】：TP18;V423

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本文編號：2029842