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算法在多目標(biāo)優(yōu)化問題中的仿真應(yīng)用
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    摘要:

    綜合了PSO算法應(yīng)用于求解多目標(biāo)問題的一些思想,,引入罰函數(shù)對(duì)約束條件進(jìn)行處理,,并利用均值的概念來計(jì)算選取PSO算法更新方程中的全局最優(yōu)值,從而使算法在最優(yōu)解的引導(dǎo)下,,跳出局部最小值,,更快地向Pareto最優(yōu)解前沿收斂,。壓縮彈簧多目標(biāo)優(yōu)化設(shè)計(jì)實(shí)例證明了所提出算法的有效性,。

    Abstract:

    In this paper, some ideas about PSO research and applications in multi-objective problem were synthesized. Based on the concept of average, the global best fitness function value in updated formula of PSO was calculated, which benefits to the algorithm escaping from the local minimums and converging quickly to the front of Pareto optimal set. Meanwhile the penalty function method was used as constraint-handling technique. The algorithm was proved effective by the simulation and experimental results for the given multi-objective optimal problem of a compression spring.

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張學(xué)良,溫淑花,李海楠,孫大剛.算法在多目標(biāo)優(yōu)化問題中的仿真應(yīng)用[J].農(nóng)業(yè)機(jī)械學(xué)報(bào),2007,38(7):112-115.[J]. Transactions of the Chinese Society for Agricultural Machinery,2007,38(7):112-115.

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