本文選題：光學優(yōu)化算法 + 適應度��； 參考：《計算機工程與應用》2017年12期

【摘要】：光學優(yōu)化算法是一種新型優(yōu)化算法,源自物理學中的光學原理。針對基本光學優(yōu)化算法中適應度函數隨進化過程恒定不變導致算法搜索能力差、精度低等不足之處,結合遺傳算法中自適應度的改進方法,提出一種可隨進化代數動態(tài)調整的非線性適應度函數,改進了光學優(yōu)化算法的適應度函數。通過一系列典型的基準函數測試了改進算法的性能,實驗結果驗證了改進算法的可行性與有效性。
[Abstract]:Optical optimization algorithm is a new optimization algorithm derived from the optical principles in physics. The fitness function of the basic optical optimization algorithm is invariant with the evolution process, which leads to the poor searching ability and low precision of the algorithm. The improved method of adaptive degree is combined with the genetic algorithm. A nonlinear fitness function, which can be dynamically adjusted with evolution algebra, is proposed, and the fitness function of optical optimization algorithm is improved. The performance of the improved algorithm is tested by a series of typical benchmark functions. The experimental results show that the improved algorithm is feasible and effective.
【作者單位】： 上海理工大學管理學院;
【基金】：國家自然科學基金(No.71401106) 上海市高峰高原學科建設項目
【分類號】：O43;TP18
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本文編號：1957638