global searching 中文意思是什麼
global searching
解釋
全局搜索-
Global convergence of non - quasi - newton merhods with a class of inexact line searching
帶一類非精確線搜索的非擬牛頓演算法的整體收斂性 -
Searching the optimal coalition structure which optimizes the total of values of coalitions is all a global problem in dps, mas and game theories
求解能極大化聯盟值總和的最佳聯盟結構是關于agents聯盟的一個全局性問題，在dps 、 mas和對策論這三個不同的領域中都有重要價值。 -
The approach not only improves global searching ability of genetic algorithm, but also enhances searching speed
這種改進方法不僅提高了遺傳演算法全局搜索能力，而且很好的提高了搜索速度。 -
Genetic algorithm is a random searching method which simulates natural selection and evolution. this method has some advantages that other usual methods do n ' t have because of its two characters - - - - - - implicit parallelism and global searching
遺傳演算法是模仿自然選擇與進化的隨機搜索方法，由於其隱含并行性和全局搜索特性，使其具有其他常規優化演算法無法擁有的優點。 -
Based on the genetic algorithm ' s global searching capability with probability regulation and euclid ' s space distance metric to settle multi - objective, the algorithm that integrates multi - objective ' s decision - making into the modified genetic algorithm to solute the optimal model with discrete variables and multi - objective is proposed. during the algorithm ' s design, the euclid ' s space distance metric is proposed to transform the multi - objective problem into single objective problem. and some modified measure to fitness function and crossover probability and mutation probability are used to improve the performance of the algorithm and avoid premature convergence
演算法設計過程中，利用歐幾里德空間距離準則和罰函數法，將含有約束條件的多目標規劃問題轉化為無約束的單目標優化問題；針對簡單遺傳演算法出現的早熟，構造隨進化代數動態調整適應度的適應度函數和隨個體適應度自適應調整的交叉、變異概率；提出比例選擇與精英保留策略相結合的選擇、兩點交叉和簡單變異的改進遺傳演算法。 -
Based on the global stochastic searching method of classic genetic algorithm ( ga ), and using the diversity preservation strategy of antibodies in biology immunity mechanism, the method greatly improves the colony diversity of ga and has better global searching capability
該演算法在傳統遺傳演算法全局隨機搜索的基礎上，借鑒了生物免疫機制中抗體的多樣性保持策略，改善了遺傳演算法的群體多樣性，具有更好的全局搜索能力。 -
It has a global searching capacity of genetic algorithm as well as effective local searching capacity
它即有遺傳演算法的全局搜索能力，又有高效的局部搜索能力。 -
Simulations prove, it saliently solves the " prematurity " problem and therefoe has better ability of global searching with the property of stabilization, agility and operating simply
模擬表明，該演算法有效的克服了早熟現象，而且演算法穩定、靈活，操作簡單。 -
The non - linear inversion methods are developed to overcome the difficulties met when the linear inversion methods are used in the geophysical inversion problems. this paper presents the simulated annealing is hopeful for the inversion problem of resistivity map reconstruction. due to the slow speed caused by the global searching, so for, the method is limited in the processing the 1 - d model
為了克服像傳統線性反演方法中解易陷入局部極小的不足，本文採用了模擬退火這種非線性方法實現了高密度電法一維反演，初步取得到較好反演結果，但由於它隨機搜索全局解空間，計算量非常大，因此目前主要應用於一維地電結構的反演。 -
Compared to the conventional amplitude - comparison angular estimation method, the proposed method can suppress the angle glint better since the angle motion parameter of target is obtained by global searching
有別于傳統比幅測角方法，所提方法通過全局搜索得到目標的角運動參數，可以較好地抑制角閃爍的發生。 -
The ability of global searching and extensive application in other scopes of genetic algorithm had proved that it is attracting goal for us to study how to apply genetic algorithm to network reconfiguration
遺傳演算法的全局搜索能力和在其它領域的應用證明，研究遺傳演算法進行網路重構應該是有巨大潛力的。這正是本論文研究的初衷。 -
Numerical computations show that the approach has global searching capability and can give satisfactory solutions, especially for the system of nonlinear equations, which is sensitive to the variables
數值計算結果表明此方法具有全局搜索性，特別是，它能夠以滿意的精度求出對未知數具有敏感性的非線性方程組的解。 -
Thus, according to mechanics of dealing with stochastic phenomena in programming theory, multi - objective stochastic programming model is developed to dispose parameter uncertainty. as a heuristic monte carlo approach with powerful global searching, genetic algorithm based on stochastic programming is utilized
為了更好的處理實際生產中參數的不確定性，根據數學規劃論中處理隨機現象的機理，建立多目標隨機規劃模型，模型求解採用基於隨機規劃的遺傳演算法。 -
Then a new improving scheme of ga which we call it hgsaa ( hybrid gentic - simulatd annealing algorithm ) is put forward based on the existing condition, and the new algorithm works more efficiently than the old one because it has not only ga ' s global searching capability but also saa ' s local searching capability. the simulatio results show the new algorithm is more effective than ga. the minimum cost is reduced and the clustered network structures have been produced with the onus are evenly distributed or clustered around the obds and the clustered network structures are well ordered with a satisfactory degree of symmetry after the hgsaa has runed 100 generation
吏為重要的是，本文充分利用了混合遺傳演算法的思想，將遺傳演算法和模擬退火演算法的優勢相結合，使得遺傳演算法的全局尋優能力與模擬退火演算法的局部尋優能力在該問題的求解過程中充分發揮了作用，由本文實現的混合遺傳一模擬退火演算法明顯地改進了演算法的效果，由此生成的網路方案的建設費用得到極大減少，同時拓撲結構的優化結果也比單純的遺傳演算法的效果好得多。 -
Our view is that gray coding not only has good global searching ability but also good local searching ability. studying on fitness scaling
本文認為，格雷碼編碼方法不但具有良好的局部搜索能力而且具有較好的全局搜索能力。 -
However, because bayes network structure learning algorithm generally explore too big a search space, as well as its computing complexity, and moreover, the such learning algorithm is very easy to fall into the local optimal solution, ban ’ s efficiency is questioned. finding a kind of highly effective global searching algorithm is an extremely important to the study of ban
針對這種分類器的結構學習，設計了結合對數似然的適應度函數，並給出了網路結構的編碼方案，設計了相應的遺傳運算元，使得該演算法能夠收斂到全局最優的結構。實驗結果表明，該演算法具有良好的分類效果。 -
The experimental research shows sega has a good performance of global searching
通過實驗表明，該演算法具有良好的全局尋優性能。 -
Gasa is characteristic of many advantages, such as the calculating robustness, implied inherent parallelism, global searching and local convergence. these advantages are integrated in gcs in our method and make the constraint problems solved robustly and efficiently. based on such approach, the ecological niche ideal is further integrated with gasa
由於遺傳模擬退火演算法本身具有很多優點：很強的計算魯棒性、隱含的內在并行性、全局搜索與局部快速收斂能力，因此將遺傳模擬退火演算法與約束求解相結合大大提高了約束求解的魯棒性和效率。 -
In this paper, an improved genetic algorithm ( ga ) was applied to the design of high performance e - plane waveguide filters at the first time. for dealing with the shortcoming of ga as low efficiency in local optimum searching, the idea based on parallel genetic algorithm ( pga ) known as separate local optimization was presented. by separating the whole population into sub - populations, and adding a new operator known as crossover between two sub - population, the new method can provide as 2 3 times quickly as traditional ga. all numerical examinations illustrated the high convergence speed and good global searching of the new algorithm, and proved the validity of it
由於遺傳演算法表現出良好的全局搜索性能，因此本文將其應用於高性能e面波導濾波器的設計中.針對傳統遺傳演算法所存在的局部搜索能力差的缺點，本文在并行遺傳演算法的基礎上，通過將原有群體劃分為多個子群體，利用多個子群進行局部極值點的搜索，並利用群間遷徙，使改進后的演算法收斂速度平均提高了2 3倍.數值實驗表明演算法具有較快的收斂速度和較好的全局搜索性能，證明了演算法的有效性 -
By dividing fabric image into many windows with the same size, choosing a window with minimum local entropy as a region of interest, thresholding the region and filtering out noise through opening function of mathematical morphology, calculating the defect shape factors as recognition parameters, a algorithm and method of detection of defects in fabric is studied : which has advantages of high identification, correctness, and fast inspection speed mainly because it can avoid complicated calculation of whole image and global searching when the feature parameters are extracted
摘要將織物圖像分成大小相同的局部窗口，選取局部熵最小的窗口為待研究的感興趣區域，在此區域內分割出疵點圖像並用數學形態學中的開運算濾除噪聲，計算疵點形狀因子等作為識別參數，研究表明此方法因能避免對整幅圖像進行復雜運算，在提取特徵參數時對圖像的全局搜索，具有識別正確率高、檢測速度快等優點。
分享友人