本文選題：社會(huì)蜘蛛群優(yōu)化算法 + 0-1背包問(wèn)題��； 參考：《廣西民族大學(xué)》2017年碩士論文

【摘要】：社會(huì)蜘蛛群優(yōu)化算法是模擬自然界蜘蛛進(jìn)行分工合作、信息交流和繁衍后代的行為而出現(xiàn)的一種新興的群智能優(yōu)化算法,該算法具有結(jié)構(gòu)簡(jiǎn)單,穩(wěn)定性較強(qiáng)和易于理解的特點(diǎn)。自從提出之后就受到該領(lǐng)域?qū)W者的廣泛關(guān)注。但隨著研究的深入,一些學(xué)者發(fā)現(xiàn)該算法存在尋優(yōu)精度較差,收斂速度較慢等缺點(diǎn),這在一定程度上限制了社會(huì)蜘蛛群優(yōu)化算法的理論發(fā)展和應(yīng)用范圍。本論文主要是針對(duì)社會(huì)蜘蛛群優(yōu)化算法中存在的一些不足進(jìn)行改進(jìn),并將改進(jìn)的優(yōu)化算法應(yīng)用于實(shí)際的優(yōu)化問(wèn)題中,目的是進(jìn)一步完善社會(huì)蜘蛛群優(yōu)化算法的理論和拓展其應(yīng)用范圍。本文的工作內(nèi)容主要分為以下五個(gè)方面:(1)提出一種利用社會(huì)蜘蛛群優(yōu)化算法求解0-1背包問(wèn)題的算法,該算法的優(yōu)勢(shì)在于求解高維的0-1背包問(wèn)題。實(shí)驗(yàn)結(jié)果表明,在求解高維的0-1背包問(wèn)題時(shí)社會(huì)蜘蛛群優(yōu)化算法具有一定的優(yōu)勢(shì)。(2)提出一種利用社會(huì)蜘蛛群優(yōu)化算法求解無(wú)線傳感器覆蓋率問(wèn)題的方法,可快速地找出無(wú)線傳感器最佳布置方案,并以可視的方式表現(xiàn)出來(lái)。實(shí)驗(yàn)結(jié)果表明,利用社會(huì)蜘蛛群優(yōu)化算法得到的優(yōu)化方案最佳。(3)提出一種基于精英反向?qū)W習(xí)策略的社會(huì)蜘蛛群優(yōu)化算法,克服社會(huì)蜘蛛群優(yōu)化算法易陷入局部最優(yōu)的缺點(diǎn),將精英反向?qū)W習(xí)策略引入社會(huì)蜘蛛群優(yōu)化算法,實(shí)現(xiàn)擴(kuò)大其搜索空間和增強(qiáng)種群多樣性的目的,并將基于精英反向?qū)W習(xí)策略的社會(huì)蜘蛛群優(yōu)化算法用于函數(shù)優(yōu)化問(wèn)題。(4)提出一種具有差分進(jìn)化算子的社會(huì)蜘蛛群優(yōu)化算法,較大程度上克服社會(huì)蜘蛛群算法在一些情況下尋優(yōu)能力較差且收斂速度較慢的缺點(diǎn),將差分進(jìn)化算子引入社會(huì)蜘蛛群優(yōu)化算法,增強(qiáng)了蜘蛛個(gè)體的全局搜索能力,進(jìn)而提升了算法的性能,并將具有差分進(jìn)化算子的社會(huì)蜘蛛群優(yōu)化算法用于流水線型生產(chǎn)車間調(diào)度問(wèn)題。(5)提出一種基于量子編碼的社會(huì)蜘蛛群優(yōu)化算法,引入量子編碼的思想,量子編碼擴(kuò)展了個(gè)體信息的多樣性,量子旋轉(zhuǎn)門作為更新策略,提升了算法的局部和全局搜索能力,且將改進(jìn)后的量子社會(huì)蜘蛛群優(yōu)化算法應(yīng)用到水電站優(yōu)化調(diào)度問(wèn)題。
[Abstract]:The social spider swarm optimization algorithm is a new kind of swarm intelligence optimization algorithm which simulates the behavior of natural spiders working together, exchanging information and breeding offspring. The algorithm has the characteristics of simple structure, strong stability and easy to understand. Since it was put forward, it has received extensive attention from scholars in this field. However, with the development of the research, some scholars find that the algorithm has some shortcomings, such as poor precision and slow convergence rate, which limits the theoretical development and application scope of the social spider swarm optimization algorithm to a certain extent. This paper mainly aims at improving some shortcomings of the social spider swarm optimization algorithm, and applies the improved optimization algorithm to the actual optimization problem. The aim is to further improve the theory of social spider swarm optimization algorithm and expand its application scope. The main work of this paper is divided into the following five aspects: 1) an algorithm for solving 0-1 knapsack problem using social spider swarm optimization algorithm is presented. The advantage of this algorithm is to solve the 0-1 knapsack problem with high dimension. The experimental results show that the social spider swarm optimization algorithm has some advantages in solving the high dimensional 0-1 knapsack problem. It can quickly find out the best arrangement of wireless sensor and display it visually. The experimental results show that a social spider swarm optimization algorithm based on elite reverse learning strategy is proposed, which overcomes the shortcoming that the social spider swarm optimization algorithm is prone to fall into local optimum. The elite reverse learning strategy is introduced into the social spider swarm optimization algorithm to expand the search space and enhance the diversity of the population. The social spider swarm optimization algorithm based on elite reverse learning strategy is applied to the function optimization problem. (4) A social spider swarm optimization algorithm with differential evolution operator is proposed. In order to overcome the shortcomings of the social spider swarm algorithm in some cases, the differential evolution operator is introduced into the social spider swarm optimization algorithm, and the global search ability of the spider individual is enhanced. Furthermore, the performance of the algorithm is improved, and the social spider swarm optimization algorithm with differential evolution operator is applied to the production shop scheduling problem of pipeline type. (5) A social spider swarm optimization algorithm based on quantum coding is proposed, and the idea of quantum coding is introduced. Quantum coding expands the diversity of individual information. Quantum rotary gate is used as an update strategy to improve the local and global search ability of the algorithm. The improved quantum society spider swarm optimization algorithm is applied to the optimal operation of hydropower station.
【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

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