發(fā)布時間：2018-07-05 03:58

  本文選題：智能算法 + 猴群算法��； 參考：《天津大學(xué)》2016年博士論文

【摘要】：智能算法是基于自然現(xiàn)象運(yùn)行機(jī)制的隨機(jī)優(yōu)化算法,具有結(jié)構(gòu)簡單、易于操作和全局優(yōu)化能力強(qiáng)等優(yōu)點(diǎn),在決策優(yōu)化、系統(tǒng)優(yōu)化、工程設(shè)計(jì)等諸多領(lǐng)域都具有廣泛應(yīng)用.然而在處理復(fù)雜優(yōu)化問題時,現(xiàn)有的智能算法依然會出現(xiàn)早熟收斂和停滯問題.為了從算法的運(yùn)行機(jī)理上探索早熟收斂和停滯問題的解決方案,基于全局探索能力和局部開發(fā)能力的有效平衡,本文分別設(shè)計(jì)了猴群算法和差分進(jìn)化算法的改善機(jī)制,并提出了一種新型智能算法.主要工作包括:(1)設(shè)計(jì)了基于自組織分層結(jié)構(gòu)和時變參數(shù)的改進(jìn)方案,用于提高猴群算法的優(yōu)化性能.在改進(jìn)方案中,利用個體的適應(yīng)值信息和優(yōu)化空間的邊界信息,同時融合提出的選擇算子、基于適應(yīng)值的替換算子和排斥算子重新設(shè)計(jì)了原始猴群算法的爬、望和跳操作;采用了分層結(jié)構(gòu)組織其核心操作,并利用設(shè)計(jì)的自組織機(jī)制協(xié)調(diào)核心操作的執(zhí)行;利用單個時變參數(shù)替代了原始猴群算法中的多個固定參數(shù),提高了算法應(yīng)用的便捷性.大量比較實(shí)驗(yàn)表明改進(jìn)方案明顯優(yōu)于原始猴群算法和7種表現(xiàn)優(yōu)異的智能算法.(2)設(shè)計(jì)了基于高斯變異和動態(tài)參數(shù)的改進(jìn)方案,用于提高差分進(jìn)化算法的優(yōu)化性能.在改進(jìn)方案中,利用隨機(jī)選擇個體的適應(yīng)值信息設(shè)計(jì)了新型高斯變異算子和改進(jìn)了一種典型變異算子,并利用累計(jì)分值信息提出了兩種變異算子之間的協(xié)作規(guī)則;分別采用余弦函數(shù)和高斯函數(shù)實(shí)現(xiàn)了縮放因子的周期性變化和交叉概率的波動性變化.大量比較實(shí)驗(yàn)表明改進(jìn)方案明顯優(yōu)于5種差分進(jìn)化算法變型和兩種表現(xiàn)優(yōu)異的群智能算法.(3)設(shè)計(jì)了基于再初始化策略和優(yōu)化空間調(diào)整策略的改善機(jī)制,用于提高差分進(jìn)化算法的優(yōu)化性能.在改善機(jī)制中,結(jié)合種群的優(yōu)化狀態(tài)和交叉算子提出了再初始化策略,用于恢復(fù)算法的全局探索能力;利用最優(yōu)個體信息和具有波動性的動態(tài)參數(shù)設(shè)計(jì)了優(yōu)化空間調(diào)整策略,用于防止再初始化策略引發(fā)的過度探索.所設(shè)計(jì)的改善機(jī)制具有算法獨(dú)立性,可以便捷的移植到各種差分進(jìn)化算法中.大量的比較實(shí)驗(yàn)表明改善機(jī)制可以有效提高多種差分進(jìn)化算法的優(yōu)化性能.(4)設(shè)計(jì)了基于軍事理論中聯(lián)合作戰(zhàn)策略的新型智能算法 聯(lián)合作戰(zhàn)算法,用于處理大規(guī)模復(fù)雜優(yōu)化問題.在聯(lián)合作戰(zhàn)算法中,利用精英個體信息和優(yōu)化空間的動態(tài)調(diào)整信息設(shè)計(jì)了攻擊操作,用于探索新區(qū)域;利用正態(tài)分布和交叉算子設(shè)計(jì)了防御操作,用于開發(fā)局部區(qū)域;利用隨機(jī)排序技術(shù)提出了整編操作,用于恢復(fù)種群多樣性.采用多個大規(guī)模復(fù)雜優(yōu)化問題進(jìn)行了全面系統(tǒng)的比較實(shí)驗(yàn),結(jié)果表明聯(lián)合作戰(zhàn)算法明顯優(yōu)于6種表現(xiàn)優(yōu)異的智能算法.
[Abstract]:Intelligent algorithm is a random optimization algorithm based on natural phenomena, which has the advantages of simple structure, easy operation and strong ability of global optimization. It is widely used in many fields, such as decision optimization, system optimization, engineering design and so on. However, in dealing with complex optimization problems, the existing intelligent algorithms will still have premature convergence and stagnation problems. In order to explore the solution of premature convergence and stagnation problem from the operation mechanism of the algorithm, based on the effective balance of global exploration ability and local development ability, this paper designs the improvement mechanism of monkey swarm algorithm and differential evolution algorithm, respectively. A new intelligent algorithm is proposed. The main works are as follows: (1) an improved scheme based on self-organizing hierarchical structure and time-varying parameters is designed to improve the optimization performance of monkey swarm algorithm. In the improved scheme, using the fitness information of individuals and the boundary information of the optimization space, the proposed selection operator is fused at the same time, and the crawling, lookout and jump operation of the original monkey group algorithm is redesigned based on the fitness replacement operator and the exclusion operator. The core operation is organized with layered structure, and the implementation of the core operation is coordinated by the self-organizing mechanism designed. A single time-varying parameter is used to replace many fixed parameters in the original monkey swarm algorithm, which improves the convenience of the algorithm application. A large number of comparative experiments show that the improved scheme is obviously superior to the original monkey swarm algorithm and seven intelligent algorithms with excellent performance. (2) an improved scheme based on Gao Si mutation and dynamic parameters is designed to improve the optimization performance of differential evolution algorithm. In the improved scheme, a new Gao Si mutation operator and a typical mutation operator are designed by using the fitness information of randomly selected individuals, and the cooperative rules between the two mutation operators are proposed by using the cumulative score information. CoSine function and Gao Si function are used to realize the periodic variation of scaling factor and the fluctuation change of crossover probability. A large number of comparative experiments show that the improved scheme is obviously superior to five differential evolution algorithms and two excellent swarm intelligence algorithms. (3) an improved mechanism based on reinitialization strategy and optimized spatial adjustment strategy is designed. It is used to improve the optimization performance of differential evolution algorithm. In the improvement mechanism, combining the optimal state of the population and the crossover operator, the reinitialization strategy is proposed to recover the global exploration ability of the algorithm, and the optimal spatial adjustment strategy is designed by using the optimal individual information and the dynamic parameters with volatility. Used to prevent over-exploration caused by reinitialization policies. The improved mechanism is algorithmic independent and can be easily transplanted to various differential evolution algorithms. A large number of comparative experiments show that the improved mechanism can effectively improve the optimization performance of various differential evolution algorithms. (4) A new intelligent algorithm based on the joint operational strategy in military theory is designed. Used to deal with large-scale complex optimization problems. In the joint operation algorithm, the attack operation is designed by using the information of the elite individual and the dynamic adjustment information of the optimized space to explore the new area, the defense operation is designed by using the normal distribution and the crossover operator to develop the local area. In this paper, an integration operation is proposed to restore population diversity using random sorting technique. A comprehensive and systematic comparison experiment is carried out with several large-scale and complex optimization problems. The results show that the joint operations algorithm is superior to the six intelligent algorithms with excellent performance.
【學(xué)位授予單位】：天津大學(xué)
【學(xué)位級別】：博士
【學(xué)位授予年份】：2016
【分類號】：TP18

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