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| 折扣與優(yōu)惠：團(tuán)購最低可5折優(yōu)惠 - 了解詳情 | 論文格式：Word格式(*.doc) |  |
摘要：微粒群算法（PSO）是繼遺傳算法后的又一個基于生物演化的隨機優(yōu)化算法，它操作簡便，收斂速度快且穩(wěn)定，使得它近年來被廣泛應(yīng)用，并在工程實驗中發(fā)揮了重要的作用。 本文先介紹了基本微粒群算法，并在基本微粒群的基礎(chǔ)上對其進(jìn)行改進(jìn)，加入了突變的部分使其能更好地收斂于全局最優(yōu)解，并將改進(jìn)后的微粒群算法引入到線性約束問題的最優(yōu)化求解中。在研究與工程中，很多問題都帶有線性約束條件，常用的方法都是先將約束問題轉(zhuǎn)為無約束問題后再進(jìn)行求解。傳統(tǒng)最優(yōu)化解約束問題時對問題模型有較多的條件限制，而微粒群算法對問題信息依賴度不高，因此，在問題轉(zhuǎn)化為無約束后，再借助微粒群算法能更方便快捷地求解。 關(guān)鍵詞：線性約束；優(yōu)化； 微粒群算法
Abstract：Particle Swarm Optimization is another new stochastic optimization after genetic algorithm which is also based on evolution. PSO is easy to perform and convergence swiftly and steadily, such good characteristic make it be applied widely in most projects recently and it have performed an important function. This article first describes the basic particle swarm algorithm, and on the basis of the basic particle swarm to improve it, adding some mutations enable it to better convergence to global optimal solution, and the improved PSO algorithm is introduced to linear constraints to solve the optimization problem. In research and engineering, many problems with linear constraints, commonly used methods are first constrained problem into unconstrained problem and then solve it. Best to resolve the issue of traditional constraints on the problem model has more constraints, while the particle swarm algorithm to the problem of information dependence is not high, therefore, is transformed into an unconstrained, then using particle swarm algorithm can solve more easily and quickly. Key words: linear constraint；optimization； particle swarm algorithm
微粒群算法是一種新興的基于群體智能的進(jìn)化算法。 本文介紹了算法基本思想、理論基礎(chǔ)，以及算法的改進(jìn)方法，并在一般微粒群的基礎(chǔ)上進(jìn)行改進(jìn), 并與拉格朗日法相結(jié)合, 研究了帶約束微粒群算法。 通過拉格朗日對偶原理, 將拉格朗日乘子分離出來進(jìn)行優(yōu)化, 使得線性約束問題通過微粒群優(yōu)化及少量迭代可得到最優(yōu)解。該算法可以用于解決工程中帶約束的問題. 最后, 通過對低通濾波器的設(shè)計, 驗證了改進(jìn)算法的有效性。 但是PSO算法數(shù)學(xué)基礎(chǔ)相對薄弱,且還存在許多不完善和未涉及到的問題,尤其對離散的組合優(yōu)化問題的研究還處于起步階段。 隨著微粒群算法理論研究的不斷深入,應(yīng)用領(lǐng)域必將會有更廣的發(fā)展。
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