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粒子群參數(shù)自適應(yīng)調(diào)整的優(yōu)化設(shè)計
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    摘要:

    在分析粒子群優(yōu)化原理基礎(chǔ)上，引入模擬退火機制以一定的概率對部分粒子的速度及位置執(zhí)行更新操作，建立了粒子群慣性量權(quán)重因子及學習因子的模糊邏輯控制器以實現(xiàn)粒子群參數(shù)的自適應(yīng)調(diào)整，從而提高優(yōu)化算法的收斂速度及獲得全局解的能力。通過運用常規(guī)優(yōu)化方法、遺傳算法及參數(shù)自適應(yīng)調(diào)整的粒子群優(yōu)化方法對起重機結(jié)構(gòu)主梁截面優(yōu)化設(shè)計對比可知：采用粒子群參數(shù)調(diào)整的優(yōu)化方法具有自適應(yīng)能力強、計算效率高及優(yōu)化設(shè)計精度高等優(yōu)點。

    Abstract:

    This paper introduced the simulated annealing mechanism into the particle swarm optimization. In the method, only a part of particles were involved in the speed and location renewal operation with certain probability. Two fuzzy logic controllers about inertia and learning factor were built in order to improve convergence speed and obtain global optimal solution. The optimization results of the main beam cross-section for crane structure from conventional optimization method, genetic algorithm and particle swarm optimization, were compared with one another. The comparison analysis indicates that the proposed particle swarm optimization method based on fuzzy logic parameter adjusting has advantages such as better self-adaptive capacity, higher computation efficiency and design accuracy.  

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劉道華,原思聰,張錦華,吳濤.粒子群參數(shù)自適應(yīng)調(diào)整的優(yōu)化設(shè)計[J].農(nóng)業(yè)機械學報,2008,39(9):134-137.[J]. Transactions of the Chinese Society for Agricultural Machinery,2008,39(9):134-137.

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