發(fā)布時間：2018-03-18 17:54

  本文選題：粒子群算法　切入點：拓?fù)浣Y(jié)構(gòu)　出處：《太原理工大學(xué)》2017年碩士論文　論文類型：學(xué)位論文

【摘要】：基于啟發(fā)式的群智能隨機(jī)演化計算,Kenndey和Eberhart通過模擬自然界中鳥群和魚群在捕食過程中的群體協(xié)作與競爭行為,于1995年提出了粒子群優(yōu)化算法。相比于其他群智能算法,粒子群優(yōu)化算法在解決多目標(biāo)優(yōu)化、動態(tài)尋優(yōu)等問題上,具有結(jié)構(gòu)簡單、易編程實現(xiàn)等特點,經(jīng)過二十多年的不斷發(fā)展,逐漸形成了一套完整的理論研究體系,已經(jīng)成為國際進(jìn)化計算領(lǐng)域的重要研究方向。粒子群優(yōu)化算法一經(jīng)提出,便受到廣泛的關(guān)注和應(yīng)用,但到了進(jìn)化后期算法存在著種群多樣性喪失、易陷入局部極值等問題,導(dǎo)致后期收斂速度減緩,優(yōu)化精度不足。為了在提高尋優(yōu)精度的同時,加快算法的收斂速度,本文從種群拓?fù)浣Y(jié)構(gòu)和進(jìn)化學(xué)習(xí)機(jī)制兩方面分別對算法進(jìn)行了改進(jìn),并將改進(jìn)算法應(yīng)用于旋風(fēng)分離器的結(jié)構(gòu)參數(shù)優(yōu)化。本文研究的主要內(nèi)容如下:1、為增強粒子群算法種群內(nèi)不同個體間的信息交流能力,考慮從種群拓?fù)浣Y(jié)構(gòu)入手,提出了一種基于混沌拓?fù)浣Y(jié)構(gòu)的全信息變異粒子群優(yōu)化算法(CFMPSO)。該算法在進(jìn)化過程中周期性地對種群的拓?fù)浣Y(jié)構(gòu)進(jìn)行混沌重組,并對各粒子鄰域中的最優(yōu)個體進(jìn)行變異,通過全信息策略對不同變異粒子信息進(jìn)行充分利用,以增強其交流能力,改善了粒子群算法的收斂性能,加快了進(jìn)化速度。實驗結(jié)果表明,CFMPSO算法在解決大多數(shù)測試函數(shù)時能獲得較好的尋優(yōu)精度,并且其函數(shù)計算次數(shù)少、尋優(yōu)速度快。2、通過深入研究算法本身的特性,對種群的尋優(yōu)機(jī)制進(jìn)行數(shù)值分析,推導(dǎo)出標(biāo)準(zhǔn)粒子群算法近似于一種比例-積分(PI)控制,由于其固有積分屬性的存在,使算法收斂速度減慢。通過添加微分項,提出微分策略的快速粒子群優(yōu)化算法,利用該策略來加快標(biāo)準(zhǔn)粒子群及其改進(jìn)算法的收斂速度,提高優(yōu)化效率。對D-SPSO和D-FIPS進(jìn)行仿真實驗對比,結(jié)果表明微分控制策略改進(jìn)算法的函數(shù)計算次數(shù)少、尋優(yōu)速度快。證明引入該策略的改進(jìn)粒子群優(yōu)化算法在解決進(jìn)化過程中出現(xiàn)的尋優(yōu)速度慢和優(yōu)化效率低的問題上,取得了良好的效果。3、本課題組將改進(jìn)粒子群算法用于旋風(fēng)分離器的結(jié)構(gòu)尺寸優(yōu)化,以旋風(fēng)分離器滿足較小壓力損失(35)p和較大分離效率η作為優(yōu)化目標(biāo),采用四因素三水平試驗條件,通過Box-Behnken設(shè)計試驗,得到其結(jié)構(gòu)尺寸的回歸方程,并利用改進(jìn)粒子群算法對結(jié)構(gòu)回歸方程進(jìn)行優(yōu)化。經(jīng)過對各算法優(yōu)化結(jié)果分析,表明各改進(jìn)算法在得到較為理想的優(yōu)化結(jié)果同時,又具有較快的優(yōu)化速度。
[Abstract]:Based on heuristic stochastic evolution algorithm of swarm intelligence, Kenndey and Eberhart proposed a particle swarm optimization algorithm in 1995 by simulating the cooperative and competitive behaviors of birds and fish during predation in nature, compared with other swarm intelligence algorithms. Particle swarm optimization (PSO) has the advantages of simple structure and easy programming to solve the problems of multi-objective optimization and dynamic optimization. After more than 20 years of continuous development, a complete theoretical research system has been gradually formed. Particle swarm optimization (PSO) has been widely concerned and applied since it was put forward, but at the late stage of evolution, it has some problems such as loss of population diversity, easy to fall into local extremum and so on. In order to improve the accuracy of optimization and improve the convergence speed of the algorithm, this paper improves the algorithm from two aspects: population topology and evolutionary learning mechanism. And the improved algorithm is applied to optimize the structure parameters of cyclone separator. The main contents of this paper are as follows: 1. In order to enhance the ability of information exchange among different individuals in the population of particle swarm optimization, the topological structure of the population is considered. In this paper, a full information mutation particle swarm optimization algorithm based on chaotic topology is proposed. The algorithm periodically recombines the topological structure of the population in the evolution process, and mutates the optimal individual in the neighborhood of each particle. In order to enhance its communication ability and improve the convergence performance of particle swarm optimization algorithm, the full information strategy is used to make full use of different mutation particle information. The experimental results show that the CFMPSO algorithm can obtain better optimization accuracy in solving most of the test functions, and its function calculation times are less, and the optimization speed is higher. 2. Through the in-depth study of the characteristics of the algorithm itself, The optimization mechanism of population is analyzed numerically, and the standard particle swarm optimization algorithm is deduced, which is similar to a kind of scale-integral PI-based control. Due to the existence of its inherent integral property, the convergence speed of the algorithm is slowed down. By adding differential terms, the convergence rate of the algorithm is reduced. A fast particle swarm optimization algorithm with differential strategy is proposed, which is used to accelerate the convergence speed of standard particle swarm optimization and its improved algorithm, and to improve the optimization efficiency. The simulation results of D-SPSO and D-FIPS are compared. The results show that the improved algorithm of differential control strategy has less function calculation times and faster searching speed. It is proved that the improved particle swarm optimization algorithm introduced this strategy can solve the problems of slow optimization speed and low optimization efficiency in the course of evolution. The improved particle swarm optimization (PSO) algorithm is applied to the optimization of the structure size of the cyclone separator. The cyclone separator satisfies the lower pressure loss of 35p and the greater separation efficiency 畏 as the optimization objective. Using four factors and three levels of test conditions, the regression equation of structure size is obtained by Box-Behnken design test, and the structure regression equation is optimized by using improved particle swarm optimization algorithm. The results show that the improved algorithms can get better optimization results and faster optimization speed at the same time.
【學(xué)位授予單位】：太原理工大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TQ051.8;TP18

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