PSO最早是由Eberhart和Kennedy[1,2]于1995年提出的,是一種基于群智能方法的演化計算技術,起源于對簡單社會系統的模擬,最初設想是模擬鳥群覓食的過程,但后來發現PSO是一種很好的優化工具。
上傳時間: 2014-11-30
上傳用戶:lingzhichao
改進的pso算法(3個m文件和一個說明文件): 本算法實現Clerc and Kennedy 2002年對pso算法的重要改進
上傳時間: 2016-10-25
上傳用戶:gtf1207
微粒群算法[PSO ] 是由Kennedy 和Eberhart等于1995 年開發的一種演化計算技術, 來源于對鳥群捕食過程的模擬。PSO同遺傳算法類似,是一種基于疊代的優化工具,但與遺傳算法使用遺傳操作子進行優化不同,利用群體中各個體之間的“協作”與“競爭”關系,根據自身及其競爭者的飛行經驗,調整自己的行為。同遺傳算法比較,PSO的優勢在于簡單容易實現并且沒有許多參數需要調整。目前已廣泛應用于函數優化,神經網絡訓練,模糊系統控制以及其他遺傳算法的應用領域。
上傳時間: 2017-03-13
上傳用戶:2525775
Particle swarm optimization (PSO) was originally designed and introduced by Eberhart and Kennedy (Ebarhart, Kennedy, 1995 Kennedy, Eberhart, 1995 Ebarhart, Kennedy, 2001). The PSO is a population based search algorithm based on the simulation of the social behavior of birds, bees or a school of fishes. This algorithm originally intends to graphically simulate the graceful and unpredictable choreography of a bird folk. Each individual within the swarm is represented by a vector in multidimensional search space.
標簽: optimization introduced originally and
上傳時間: 2017-09-08
上傳用戶:hoperingcong
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標簽: membership Internet changing dances
上傳時間: 2015-03-08
上傳用戶:xhz1993
粒子群優化算法(PSO)是一種進化計算技術(evolutionary computation),有Eberhart博士和Kennedy博士發明。源于對鳥群捕食的行為研究 PSO同遺傳算法類似,是一種基于疊代的優化工具。系統初始化為一組隨機解,通過疊代搜尋最優值。但是并沒有遺傳算法用的交叉(crossover)以及變異(mutation)。而是粒子在解空間追隨最優的粒子進行搜索。詳細的步驟以后的章節介紹 同遺傳算法比較,PSO的優勢在于簡單容易實現并且沒有許多參數需要調整。目前已廣泛應用于函數優化,神經網絡訓練,模糊系統控制以及其他遺傳算法的應用領域
標簽: evolutionary computation PSO 粒子群
上傳時間: 2016-04-26
上傳用戶:zhuimenghuadie
This book is the product of a twenty-year resarch project at Rice University to develop fundamental compiler technologies for vector and parallel computer systems. The effort was by Randy Allen and Ken Kennedy with the help of David Kuck and Michael Wolfe.
標簽: fundamental twenty-year University develop
上傳時間: 2016-10-16
上傳用戶:yiwen213
粒子群優化算法(PSO)是一種進化計算技術(evolutionary computation),1995 年由Eberhart 博士和Kennedy 博士提出,源于對鳥群捕食的行為研究 。
標簽: 粒子群算法
上傳時間: 2017-09-18
上傳用戶:yinfanghao