本文關(guān)鍵詞：基于信賴域的粒子群優(yōu)化算法研究　出處：《江蘇大學(xué)》2017年碩士論文　論文類(lèi)型：學(xué)位論文

  更多相關(guān)文章： 粒子群優(yōu)化算法 信賴域 柯西變異 種群多樣性

【摘要】：由于粒子群優(yōu)化算法具有易于理解與實(shí)現(xiàn)、收斂速度快、可調(diào)參數(shù)少、對(duì)適應(yīng)度函數(shù)要求低以及較好的全局搜索能力等優(yōu)點(diǎn),它已經(jīng)廣泛地應(yīng)用于科學(xué)研究和工程實(shí)踐等領(lǐng)域。但是與其他隨機(jī)優(yōu)化技術(shù)一樣,粒子群優(yōu)化算法也存在自身的缺陷,搜索方向存在盲目性、后期收斂速度慢、容易失去種群多樣性而陷入局部極優(yōu)等。為了提高粒子群優(yōu)化算法的尋優(yōu)性能,適當(dāng)?shù)脑陔S機(jī)搜索中引入確定性搜索可以提高算法的搜索效率。信賴域方法在一定條件下具有快速的局部收斂性和理想的總體收斂性,且具有穩(wěn)定的數(shù)值性能。通過(guò)在粒子群優(yōu)化算法中引入信賴域方法以引導(dǎo)粒子朝更優(yōu)的方向搜索,不但能夠保證局部收斂,加快收斂速率,還具有很高的確定性。同時(shí),為了保持種群多樣性,借鑒信賴域的思想和變異算子的優(yōu)勢(shì),在粒子陷入局部極優(yōu)的時(shí)候,實(shí)施基于信賴域技術(shù)的柯西變異,幫助粒子逃離局部最優(yōu),以提高算法的全局尋優(yōu)性能。本文將基于信賴域的算法與粒子群優(yōu)化算法結(jié)合起來(lái)以改善粒子群的搜索能力,提出了兩類(lèi)改進(jìn)的混合粒子群優(yōu)化算法,在保證有效的問(wèn)題搜索空間的條件下,提高了搜索效率和精度。本文主要工作如下:(1)提出了一種基于信賴域的吸引排斥粒子群優(yōu)化算法。該算法在ARPSO保持種群多樣性的基礎(chǔ)上,使用信賴域方法進(jìn)行局部搜索,利用獲得的潛在最優(yōu)解來(lái)調(diào)整搜索方向,避免了盲目的重復(fù)搜索。相對(duì)于標(biāo)準(zhǔn)的粒子群算法及其他幾種改進(jìn)算法,實(shí)驗(yàn)表明,該算法在收斂精度和穩(wěn)定性上取得了較好的效果,且需要更少的迭代次數(shù)。除此之外,本章還從理論上分析了新算法能夠以更高的概率收斂到全局最優(yōu)點(diǎn)。(2)基于社會(huì)階級(jí)的思想和信賴域技術(shù),提出了一種基于信賴域技術(shù)變異的隨機(jī)重組分級(jí)粒子群優(yōu)化算法。該算法根據(jù)現(xiàn)代社會(huì)階級(jí)的思想將種群分為三個(gè)不同級(jí)別,較高的級(jí)別主要負(fù)責(zé)全局性勘探,期待發(fā)現(xiàn)最優(yōu)解所在的區(qū)域,同時(shí)在中層階級(jí)中引入基于信賴域技術(shù)的柯西變異,保證群體多樣性避免陷入局部極優(yōu),導(dǎo)致“早熟”,喪失繼續(xù)搜索的能力。最下層的粒子群接受上層粒子的領(lǐng)導(dǎo),分群進(jìn)行局部精細(xì)化搜索,加快收斂速率,提高收斂精度。相比于前面提出的改進(jìn)算法,該算法不要求計(jì)算搜索方向,降低了計(jì)算的復(fù)雜度,并對(duì)目標(biāo)函數(shù)沒(méi)有解析性要求,實(shí)驗(yàn)結(jié)果表明,改進(jìn)的粒子群優(yōu)化算法明顯優(yōu)于標(biāo)準(zhǔn)粒子群算法及其相關(guān)改進(jìn)。本文通過(guò)對(duì)粒子群優(yōu)化算法原理的深入討論與分析,引入確定性信賴域方法發(fā)現(xiàn)潛在的最優(yōu)解方向,較好地避免粒子盲目重復(fù)的搜索以及借鑒社會(huì)階級(jí)分工的思想,結(jié)合變異操作保證了整個(gè)種群的全局搜索性能和收斂能力。本文工作為基于混合搜索的粒子群優(yōu)化算法的性能改進(jìn)提供了新的思路。
[Abstract]:Particle swarm optimization (PSO) has been widely applied in scientific research and engineering practice because of its advantages of easy understanding and implementation, fast convergence speed, few adjustable parameters, low requirement for fitness function and better global search ability. However, like other stochastic optimization techniques, particle swarm optimization algorithm also has its own shortcomings, such as blindness in search direction, slow convergence in later stage, loss of population diversity and fall into local optimum. In order to improve the optimization performance of particle swarm optimization (PSO), a proper search in random search can improve the efficiency of the algorithm. The trust region method has fast local convergence and ideal overall convergence under certain conditions, and has stable numerical performance. By introducing trust region method into particle swarm optimization algorithm, we can guide particles to search in a better direction. It not only ensures local convergence, but also has high certainty rate. At the same time, in order to maintain the diversity of population, learn from the idea of trust region and the advantage of mutation operator, we implement the Cauchy mutation based on trust region technology when particles fall into local optimum, and help particles escape local optimum, so as to improve the global optimization performance of the algorithm. In this paper, the trust region algorithm and particle swarm optimization algorithm are combined to improve the search ability of particle swarm. Two improved hybrid particle swarm optimization algorithms are proposed. Under the condition of ensuring effective search space, the efficiency and accuracy of search are improved. The main work of this paper is as follows: (1) a kind of particle swarm optimization (PSO) algorithm based on trust region is proposed. Based on ARPSO maintaining population diversity, the algorithm uses local search based on trust region method, and adjusts search direction by using the potential optimal solution, avoiding blind repeated search. Compared with standard particle swarm optimization algorithm and several other improved algorithms, experiments show that the algorithm achieves better results in convergence accuracy and stability, and requires fewer iterations. In addition, this chapter also theoretically analyses that the new algorithm can converge to the global best advantage with higher probability. (2) based on the idea of social class and trust region technology, a random recombinant particle swarm optimization (PSO) algorithm based on trust region technology variation is proposed. The algorithm is based on modern social class thought of population can be divided into three different levels, the higher level is mainly responsible for the overall exploration, expecting to find the optimal solution region, while the middle class in the introduction of Cauchy mutation technology based on trust region, ensure diversity of the population to avoid falling into a local optimum, lead to "premature", the loss of the ability to search. The lower layer of particle group accepts the leadership of the upper layer particle, and divides the local fine search to speed up the convergence rate and improve the convergence precision. Compared with the improved algorithm proposed previously, the algorithm does not require the computation of search direction, reduces the computational complexity and does not require the resolution of the objective function. Experimental results show that the improved particle swarm optimization algorithm is superior to the standard particle swarm optimization algorithm and its related improvements. Based on the in-depth discussion and analysis of the principle of particle swarm optimization algorithm, we introduce deterministic trust region method to find the direction of potential optimal solution, can avoid blind duplicate search and particle reference social class division thought, combined with mutation operation to ensure global search ability and the convergence ability of the whole population. This work provides a new idea for the performance improvement of particle swarm optimization (PSO) based on mixed search.
【學(xué)位授予單位】：江蘇大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類(lèi)號(hào)】：TP18

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本文編號(hào)：1338552