【摘要】：鴿群優(yōu)化算法是新的啟發(fā)式算法,是由段海濱教授等人于2014年首次提出。鴿群算法的思想是模擬鴿群利用地球磁場和地標(biāo)組合來歸巢的過程。鴿群算法具有原理相對(duì)簡單、所需調(diào)整的參數(shù)較少、比較容易實(shí)現(xiàn)等特點(diǎn)。還有計(jì)算相對(duì)簡單,魯棒性較強(qiáng)等顯著優(yōu)勢,相對(duì)于其他部分算法而言還有收斂速度較快的優(yōu)勢。與此同時(shí)鴿群優(yōu)化算法還存在不足之處,該算法有收斂精度偏低,容易出現(xiàn)局部最優(yōu)的情況,穩(wěn)定性較差等缺點(diǎn)。因此,鴿群優(yōu)化算法在理論方面和應(yīng)用方面,是有待于更深入的研究和更廣泛的擴(kuò)展。本文針對(duì)鴿群優(yōu)化算法所存在的不足進(jìn)行了分析,從添加收斂因子、位置因子、速度因子和子群變異策略等方面對(duì)鴿群算法進(jìn)行了改進(jìn),還將改進(jìn)后的算法應(yīng)用到實(shí)際的優(yōu)化問題當(dāng)中。所涉及的主要工作內(nèi)容將總結(jié)為以下3個(gè)方面:(1)采用添加收斂因子、增加位置因子和速度因子等策略對(duì)鴿群算法進(jìn)行改進(jìn)。不僅增強(qiáng)鴿子的飛行活力,提高鴿子種群的多樣性,并且能夠有效的避免鴿群的早熟收斂現(xiàn)象,使鴿群優(yōu)化算法具有一定的競爭力。并且完成了改進(jìn)后的鴿群優(yōu)化算法的相關(guān)標(biāo)準(zhǔn)函數(shù)優(yōu)化的測試。(2)通過添加子群變異策略對(duì)鴿群優(yōu)化算法進(jìn)行改進(jìn),將子群變異策略的思想應(yīng)用到鴿群優(yōu)化算法中,克服了鴿群優(yōu)化算法容易早熟收斂情況,還增大了鴿子種群潛在的搜索空間。為了增強(qiáng)鴿群優(yōu)化算法的局部搜索能力,還對(duì)貪心策略進(jìn)行引入,并且將改進(jìn)后的鴿群優(yōu)化算法應(yīng)用于0-1背包問題的求解。(3)通過把鴿群優(yōu)化算法和模擬退火算法進(jìn)行互補(bǔ)融合。結(jié)合后的算法不僅僅具有鴿群算法的特點(diǎn),還可以根據(jù)概率性進(jìn)行劣向的轉(zhuǎn)移,并且以一定的概率去接受劣解,可以讓鴿群優(yōu)化算法跳出局部最優(yōu)解的情況,從而達(dá)到實(shí)現(xiàn)全局最優(yōu)的目的。在與算法融合的基礎(chǔ)上還對(duì)鴿群優(yōu)化算法引入自適應(yīng)溫度衰變系數(shù),可以根據(jù)當(dāng)前的環(huán)境自動(dòng)調(diào)整搜索條件,來達(dá)到提高搜索效率的目的。本章還將改進(jìn)后的鴿群算法應(yīng)用于求解無人潛水器路徑規(guī)劃的問題當(dāng)中,以此來增加改進(jìn)后的鴿群算法的應(yīng)用范圍,同時(shí)也表明了算法的有效性和可行性。
[Abstract]:Pigeon swarm optimization, a new heuristic algorithm, was first proposed by Professor Duan Haibin in 2014. The idea of pigeon swarm algorithm is to simulate the homing process of pigeon swarm using the combination of geomagnetic field and landmarks. Pigeon swarm algorithm has the advantages of relatively simple principle, few parameters to be adjusted and easy to implement. There are also obvious advantages such as relatively simple computation and strong robustness, and the advantages of faster convergence rate compared with other algorithms. At the same time, the pigeon swarm optimization algorithm has some shortcomings, such as low convergence accuracy, local optimum and poor stability. Therefore, pigeon swarm optimization algorithm needs to be further studied and extended in theory and application. This paper analyzes the shortcomings of pigeon swarm optimization algorithm and improves the algorithm from the aspects of adding convergence factor, position factor, speed factor and subgroup mutation strategy. The improved algorithm is also applied to practical optimization problems. The main work involved will be summarized as follows: (1) improving pigeon swarm algorithm by adding convergence factor, increasing position factor and speed factor. It can not only enhance the flight vitality of pigeons, improve the diversity of pigeon population, but also effectively avoid the phenomenon of premature convergence of pigeon population, so that the pigeon swarm optimization algorithm has certain competitiveness. And completed the improved pigeon swarm optimization algorithm related standard function optimization test. (2) by adding subgroup mutation strategy to improve the pigeon swarm optimization algorithm, the idea of subgroup mutation strategy is applied to pigeon swarm optimization algorithm. It overcomes the premature convergence of pigeon swarm optimization algorithm and increases the potential search space of pigeon population. In order to enhance the local search ability of pigeon swarm optimization algorithm, the greedy strategy is also introduced, and the improved pigeon swarm optimization algorithm is applied to solve the 0-1 knapsack problem. (3) the pigeon swarm optimization algorithm is combined with simulated annealing algorithm to solve the 0-1 knapsack problem. The combined algorithm not only has the characteristics of pigeon swarm algorithm, but also can transfer the bad solution according to the probability, and accept the inferior solution with a certain probability, so that the pigeon swarm optimization algorithm can jump out of the local optimal solution. In order to achieve the goal of global optimization. Based on the fusion with the algorithm, the adaptive temperature decay coefficient is introduced to the pigeon swarm optimization algorithm, which can automatically adjust the search conditions according to the current environment to achieve the purpose of improving the search efficiency. In this chapter, the improved pigeon swarm algorithm is applied to solve the path planning problem of unmanned submersible vehicle, so as to increase the scope of application of the improved pigeon swarm algorithm, and also show the effectiveness and feasibility of the algorithm.
【學(xué)位授予單位】：廣西民族大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TP18

【參考文獻(xiàn)】

相關(guān)期刊論文 前10條

1 段海濱;葉飛;;鴿群優(yōu)化算法研究進(jìn)展[J];北京工業(yè)大學(xué)學(xué)報(bào);2017年01期

2 袁晗;徐春梅;楊平;許姍姍;;一種基于子群變異的粒子群優(yōu)化算法[J];計(jì)算機(jī)應(yīng)用研究;2017年04期

3 任作琳;田雨波;孫菲艷;;具有強(qiáng)開發(fā)能力的風(fēng)驅(qū)動(dòng)優(yōu)化算法[J];計(jì)算機(jī)科學(xué);2016年01期

4 段海濱;邱華鑫;范彥銘;;基于捕食逃逸鴿群優(yōu)化的無人機(jī)緊密編隊(duì)協(xié)同控制[J];中國科學(xué):技術(shù)科學(xué);2015年06期

5 李佩澤;王姍姍;樊巖;;基于改進(jìn)蝙蝠算法的背包問題求解[J];計(jì)算機(jī)應(yīng)用研究;2015年11期

6 蘭少峰;劉升;;布谷鳥搜索算法研究綜述[J];計(jì)算機(jī)工程與設(shè)計(jì);2015年04期

7 楊震;馬天寶;余文;李艷梅;;廣義分子計(jì)算模型在0-1背包問題中的應(yīng)用[J];計(jì)算機(jī)科學(xué);2014年S2期

8 李枝勇;馬良;張惠珍;;函數(shù)優(yōu)化的量子蝙蝠算法[J];系統(tǒng)管理學(xué)報(bào);2014年05期

9 陳信;周永權(quán);;基于猴群算法和單純法的混合優(yōu)化算法[J];計(jì)算機(jī)科學(xué);2013年11期

10 李輝;郭怡;;遺傳算法及其優(yōu)化[J];河南農(nóng)業(yè);2013年20期

相關(guān)博士學(xué)位論文 前2條

1 張?zhí)m華;復(fù)雜網(wǎng)絡(luò)建模的仿真與應(yīng)用研究[D];大連理工大學(xué);2013年

2 宋勝利;混合粒子群協(xié)同優(yōu)化算法及其應(yīng)用研究[D];華中科技大學(xué);2009年

相關(guān)碩士學(xué)位論文 前4條

1 李寧;基于網(wǎng)絡(luò)傳輸?shù)乃男盹w行器在森林防火中的應(yīng)用研究[D];山東大學(xué);2015年

2 韓月嬌;粒子群算法的改進(jìn)及其在BP神經(jīng)網(wǎng)絡(luò)中的應(yīng)用[D];南昌航空大學(xué);2012年

3 田輝輝;智能優(yōu)化算法的改進(jìn)及其在多維空間譜估計(jì)中的應(yīng)用[D];哈爾濱工業(yè)大學(xué);2008年

4 史今馳;背包問題的實(shí)用求解算法研究[D];山東大學(xué);2005年

，

本文編號(hào)：2293280