朱泓勳

以改良式群體智慧演算法應用於無人戰鬥飛行載具之最佳路徑規劃Optimal path planning for unmanned combat flight vehicles with improved swarm intelligence algorithm

By 朱泓勳 on February 19, 2019

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以改良式群體智慧演算法應用於無人戰鬥飛行載具之最佳路徑規劃
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採用三種改良型之群體智慧演算法來應用於無人戰鬥飛行載具(UCAV)之最佳路徑規劃分析上,以達成UCAV於飛行時能有效地避開敵方威脅源的偵測或攻擊並安全抵達設定的目的地執行軍事任務。此三種改良式演算法分別為動量型式粒子群體最佳化、自調式布穀鳥搜尋及排序為主的蜂群演算法。模擬分析包含具有不同威脅源的數量和分佈、飛行區域的範圍以及無人戰鬥飛行載具的起始點位置,所得到的最佳路徑將與其他演化式演算法的計算結果做比較。 https://drive.google.com/open?id=1tIcCWuLy49pDVc40xPd4FetO0fvkIfci
Three improved group intelligence algorithms are applied to the optimal path planning analysis of unmanned combat flying vehicles (UCAV) to achieve UCAV's ability to effectively avoid enemy threat sources from detecting or attacking during flight. Arrive at a set destination to perform a military mission. The three improved algorithms are the bee colony algorithm based on momentum type particle group optimization, self-adjusting cuckoo search and sorting. The simulation analysis includes the number and distribution of different threat sources, the range of the flight area, and the starting point position of the unmanned combat flying vehicle. The resulting optimal path will be compared with the calculation results of other evolutionary algorithms. https://drive.google.com/open?id=1tIcCWuLy49pDVc40xPd4FetO0fvkIfci

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