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翻訳と辞書　辞書検索 [ 開発暫定版 ] スポンサード リンク Flower pollination algorithm ： ウィキペディア英語版 Flower pollination algorithm Flower pollination algorithm (FPA) is a metaheuristic algorithm that was developed by Xin-She Yang,〔Xin-She Yang, Flower pollination algorithm for global optimization, Unconventional Computation and Natural Computation, Lecture Notes in Computer Science, Volume 7445, pp. 240-249 (2012).〕 based on the pollination process of flowering plants. FPA has been applied to solve practical problems in engineering,〔X. S. Yang, M. Karamanoglu, X. S. He, Flower pollination algorithm: A novel approach for multiobjective optimization, Engineering Optimization, Vol. 46, No. 9, 1222-1237 (2014).〕 solar PV parameter estimation,〔D. F. Alam, D. A. Yousri, M. B. Eteiba, Flower pollination algorithm based solar PV parameter estimation, Energy Conversion and Management, Vol. 101, pp. 410-422 (2015)〕 and fuzzy selection for dynamic economic dispatch.〔H. M. Dubey, M. Pandit, B.K. Panigraphi, Hybrid flower pollination algorithm with time-varying fuzzy selection mechanism for wind integrated multi-objective dynamic economic dispatch, Renewable Energy, Vol. 83, pp. 188-202 (2015).〕 == Main Characteristics == This algorithm has 4 rules or assumptions: 1. Biotic and cross-pollination is considered as global pollination process with pollen carrying pollinators performing Levy flights. 2. Abiotic and self-pollination are considered as local pollination. 3. Flower constancy can be considered as the reproduction probability is proportional to the similarity of two flowers involved. 4. Local pollination and global pollination is controlled by a switch probability $p\; \backslash in\; (1)$. Due to the physical proximity and other factors such as wind, local pollination can have a significant fraction p in the overall pollination activities. These rules can be translated into the following updating equations: :$x\_i^=x\_i^t\; +\; L\; (x\_i^t-g\_$ *) :$x\_i^=x\_i^t\; +\; \backslash epsilon\; (x\_i^t-x\_k^t)$ where $x\_i^t$ is the solution vector and $g\_$ * is the current best found so far during iteration. The switch probability between two equations during iterations is $p$. In addition, $\backslash epsilon$ is a random number drawn from a uniform distribution. $L$ is a step size drawn from a Lévy distribution. Lévy flights using Lévy steps is a powerful random walk because both global and local search capabilities can be carried out at the same time. In contrast with standard Random walks, Lévy flights have occasional long jumps, which enable the algorithm to jump out any local valleys. Lévy steps obey the following approximation: :$L\; \backslash sim\; \backslash frac\{|v|^\{1+\backslash beta\}\},$ with :$u\; \backslash sim\; N(0,\; \backslash sigma^2),\; \backslash quad\; v\; \backslash sim\; N(0,1),$ where $\backslash sigma$ is a function of $\backslash beta$. A matlab demo program is available for function optimization〔X. S. Yang,http://www.mathworks.com/matlabcentral/fileexchange/45112-flower-pollination-algorithm〕 抄文引用元・出典: フリー百科事典『 ウィキペディア（Wikipedia）』 ■ウィキペディアで「Flower pollination algorithm」の詳細全文を読む スポンサード リンク 翻訳と辞書 : 翻訳のためのインターネットリソース Copyright(C) kotoba.ne.jp 1997-2016. All Rights Reserved.