人工魚群智能優(yōu)化算法的研究及應(yīng)用
發(fā)布時間：2019-03-13 08:00
【摘要】：傳統(tǒng)優(yōu)化算法常常對最優(yōu)化問題的解析性質(zhì)有特定要求,難以通用地求解各類復(fù)雜多樣的最優(yōu)化問題。近年來的研究發(fā)現(xiàn),群體智能優(yōu)化算法在解決此類優(yōu)化問題方面顯示出特殊的優(yōu)勢,受到了許多學(xué)者的關(guān)注和重視。作為一種模仿魚群覓食的智能優(yōu)化算法,人工魚群(Artificial Fish Swarm Algorithm,AFSA)算法不要求目標(biāo)函數(shù)具備特有的解析性質(zhì),并對初值及參數(shù)值不敏感,具有并行處理能力以及良好的隨機(jī)性等特點,目前已得到了廣泛的研究和應(yīng)用。本文針對人工魚群算法進(jìn)行了研究和改進(jìn),并將其應(yīng)用在解決物流配送中心選址問題,具體內(nèi)容概括如下:(1)人工魚群算法存在易陷入局部極值、尋優(yōu)精度不高等問題。針對這些問題,本文提出了反向自適應(yīng)高斯變異的人工魚群算法(Opposite Adaptive and Gauss Mutation Artificial Fish Swarm Algorithm,OAGMAFSA)。該算法通過引入反向解來調(diào)整人工魚的移動方向及位置,提供更多的機(jī)會發(fā)掘潛在的較優(yōu)空間,使人工魚群能夠快速跳出局部最優(yōu);同時為了更好地平衡全局搜索與局部搜索之間的關(guān)系,使用了一種非線性自適應(yīng)視野步長策略;再者,為了增加魚群的多樣性,降低人工魚陷入早熟的可能性,提出了一種最優(yōu)解引導(dǎo)的高斯變異機(jī)制。仿真實驗結(jié)果表明,該算法能有效地提高人工魚群的尋優(yōu)精度,并且避免了人工魚群過早收斂。(2)研究發(fā)現(xiàn),基本人工魚群算法主要存在兩方面不足:(1)人工魚群算法無法根據(jù)魚群在覓食過程中的魚群分布情況自適應(yīng)地控制視野和步長;(2)人工魚群算法中的每個人工魚的行為屬于局部搜索,缺少全局性。為此,本文提出了精英學(xué)習(xí)的多維動態(tài)自適應(yīng)人工魚群算法(Elite Learning-based Multi-dimensional dynamic Adaptive Artificial Fish Swarm Algorithm,EMAAFSA)。算法通過為每個維度設(shè)定獨立的視野和步長,從而定義了視野向量、步長矩陣及多維鄰域,以此改進(jìn)了魚群的4種基本行為,使人工魚個體能夠根據(jù)魚群分布情況自適應(yīng)調(diào)整尋優(yōu)范圍。同時,提出了一種人工魚精英學(xué)習(xí)策略增加了魚群的全局性,降低了人工魚陷入局部最優(yōu)的可能性。仿真實驗結(jié)果表明,該算法能有效地提高人工魚群的尋優(yōu)質(zhì)量、魯棒性,且提高了人工魚群的全局搜索能力。(3)物流配送中心是負(fù)責(zé)貯藏各類貨物,并且進(jìn)行物品配送、裝卸等工作的物品流通中心。多物流配送中心選址問題是帶約束的非線性規(guī)劃問題,本文將提出的人工魚群優(yōu)化算法應(yīng)用于多物流配送中心選址問題中,并通過仿真實驗得到了較好的結(jié)果,證明了OAGMAFSA和EMAAFSA算法有較高的有效性和應(yīng)用價值。
[Abstract]:Traditional optimization algorithms often have specific requirements for the analytical properties of optimization problems, and it is difficult to solve all kinds of complex optimization problems in general. In recent years, it has been found that swarm intelligence optimization algorithm has special advantages in solving this kind of optimization problems, and has been paid attention to by many scholars. As an intelligent optimization algorithm, artificial fish swarm (Artificial Fish Swarm Algorithm,AFSA (artificial Fish Swarm) algorithm does not require special analytic properties of objective function, and is not sensitive to initial value and parameter value. With the characteristics of parallel processing ability and good randomness, it has been widely studied and applied. In this paper, the artificial fish swarm algorithm is studied and improved, and it is applied to solve the location problem of logistics distribution center. The concrete contents are summarized as follows: (1) the artificial fish swarm algorithm is easy to fall into the local extremum, and the optimization precision is not high. In order to solve these problems, an artificial fish swarm algorithm (Opposite Adaptive and Gauss Mutation Artificial Fish Swarm Algorithm,OAGMAFSA) based on reverse adaptive Gao Si mutation is proposed in this paper. In this algorithm, reverse solution is introduced to adjust the direction and position of artificial fish, which provides more opportunities to explore the potential optimal space, so that the artificial fish can jump out of the local optimum quickly. At the same time, in order to better balance the relationship between global search and local search, a nonlinear adaptive horizon step-size strategy is used. Furthermore, in order to increase the diversity of fish and reduce the possibility of artificial fish falling into early maturity, a mechanism of Gao Si variation guided by optimal solution was proposed. The simulation results show that the proposed algorithm can effectively improve the optimization accuracy of artificial fish and avoid premature convergence of artificial fish. (2) it is found that the proposed algorithm can effectively improve the optimization accuracy of artificial fish stocks and avoid premature convergence of artificial fish stocks. There are two main shortcomings in the basic artificial fish swarm algorithm: (1) the artificial fish swarm algorithm cannot control the field of view and the step size adaptively according to the fish colony distribution in the feeding process; (2) the behavior of each artificial fish in artificial fish swarm algorithm belongs to local search and lacks of global. Therefore, a multi-dimensional dynamic adaptive artificial fish swarm algorithm (Elite Learning-based Multi-dimensional dynamic Adaptive Artificial Fish Swarm Algorithm,EMAAFSA) for elite learning is proposed in this paper. By setting an independent field of view and step size for each dimension, the algorithm defines the visual field vector, step matrix and multi-dimensional neighborhood, thus improving the four basic behaviors of fish stocks. The artificial fish can adjust the optimal range adaptively according to the distribution of fish stocks. At the same time, an elite learning strategy for artificial fish is proposed, which increases the overall quality of fish and reduces the possibility of artificial fish falling into local optimization. The simulation results show that the algorithm can effectively improve the optimization quality, robustness and global search ability of artificial fish herd. (3) the logistics distribution center is responsible for storing all kinds of goods and delivering goods. An article circulation center for handling, etc. Multi-logistics distribution center location problem is a nonlinear programming problem with constraints. In this paper, the artificial fish swarm optimization algorithm is applied to multi-logistics distribution center location problem, and good results are obtained through simulation experiments. The validity and application value of OAGMAFSA and EMAAFSA algorithms are proved.
【學(xué)位授予單位】：江南大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TP18
本文編號：2439194
[Abstract]:Traditional optimization algorithms often have specific requirements for the analytical properties of optimization problems, and it is difficult to solve all kinds of complex optimization problems in general. In recent years, it has been found that swarm intelligence optimization algorithm has special advantages in solving this kind of optimization problems, and has been paid attention to by many scholars. As an intelligent optimization algorithm, artificial fish swarm (Artificial Fish Swarm Algorithm,AFSA (artificial Fish Swarm) algorithm does not require special analytic properties of objective function, and is not sensitive to initial value and parameter value. With the characteristics of parallel processing ability and good randomness, it has been widely studied and applied. In this paper, the artificial fish swarm algorithm is studied and improved, and it is applied to solve the location problem of logistics distribution center. The concrete contents are summarized as follows: (1) the artificial fish swarm algorithm is easy to fall into the local extremum, and the optimization precision is not high. In order to solve these problems, an artificial fish swarm algorithm (Opposite Adaptive and Gauss Mutation Artificial Fish Swarm Algorithm,OAGMAFSA) based on reverse adaptive Gao Si mutation is proposed in this paper. In this algorithm, reverse solution is introduced to adjust the direction and position of artificial fish, which provides more opportunities to explore the potential optimal space, so that the artificial fish can jump out of the local optimum quickly. At the same time, in order to better balance the relationship between global search and local search, a nonlinear adaptive horizon step-size strategy is used. Furthermore, in order to increase the diversity of fish and reduce the possibility of artificial fish falling into early maturity, a mechanism of Gao Si variation guided by optimal solution was proposed. The simulation results show that the proposed algorithm can effectively improve the optimization accuracy of artificial fish and avoid premature convergence of artificial fish. (2) it is found that the proposed algorithm can effectively improve the optimization accuracy of artificial fish stocks and avoid premature convergence of artificial fish stocks. There are two main shortcomings in the basic artificial fish swarm algorithm: (1) the artificial fish swarm algorithm cannot control the field of view and the step size adaptively according to the fish colony distribution in the feeding process; (2) the behavior of each artificial fish in artificial fish swarm algorithm belongs to local search and lacks of global. Therefore, a multi-dimensional dynamic adaptive artificial fish swarm algorithm (Elite Learning-based Multi-dimensional dynamic Adaptive Artificial Fish Swarm Algorithm,EMAAFSA) for elite learning is proposed in this paper. By setting an independent field of view and step size for each dimension, the algorithm defines the visual field vector, step matrix and multi-dimensional neighborhood, thus improving the four basic behaviors of fish stocks. The artificial fish can adjust the optimal range adaptively according to the distribution of fish stocks. At the same time, an elite learning strategy for artificial fish is proposed, which increases the overall quality of fish and reduces the possibility of artificial fish falling into local optimization. The simulation results show that the algorithm can effectively improve the optimization quality, robustness and global search ability of artificial fish herd. (3) the logistics distribution center is responsible for storing all kinds of goods and delivering goods. An article circulation center for handling, etc. Multi-logistics distribution center location problem is a nonlinear programming problem with constraints. In this paper, the artificial fish swarm optimization algorithm is applied to multi-logistics distribution center location problem, and good results are obtained through simulation experiments. The validity and application value of OAGMAFSA and EMAAFSA algorithms are proved.
【學(xué)位授予單位】：江南大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TP18
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