基于TS模糊推理的粒子群算法
發(fā)布時間：2018-10-25 06:53
【摘要】：粒子群優(yōu)化算法(Particle Swarm Optimization PSO)是一種新興的群體智能優(yōu)化算法,具有分布式、協(xié)同合作性、自組織性和實現(xiàn)簡單等特點,這使得該算法能夠在全局信息缺乏時能夠迅速地處理各種復雜問題,也為典型的復雜性問題的求解開辟了新的途徑,但該算法在處理高維復雜問題時仍有相當大的可能陷入局部最優(yōu),如何通過保障Exploration和Exploitation之間的均衡來加強全局搜索能力,是該領(lǐng)域的研究熱點和難點。從兩個方面對PSO算法進行了改進,其一是基于孫俊等人的量子行為粒子群優(yōu)化算法(Quantum-behaved Particle Swarm Optimization QPSO),提出了基于Takagi-Sugeno(TS)模糊推理的自適應(yīng)量子行為粒子群優(yōu)化算法(Adaptive Quantum-behaved Particle Swarm Optimization AQPSO),在慣性權(quán)重和種群多樣性上對粒子群優(yōu)化算法進行了改進。該算法利用群體分布和探索進程信息,由TS模糊推理動態(tài)地調(diào)整算法參數(shù)及其迭代方式,從而保證種群在更大的空間探索,減少陷入局部最優(yōu)的概率。其二是基于Riget等人提出的attractive and repulsive PSO(ARPSO)算法,提出了動態(tài)地調(diào)整慣性權(quán)重的算法(Dynamic attractive and repulsive PSO DARPSO),該算法不是簡單地用線性遞減策略,而是根據(jù)粒子是收縮狀態(tài)還是擴張狀態(tài)而動態(tài)地調(diào)整慣性權(quán)重,同時根據(jù)TS模糊推理設(shè)計了一種新的粒子位置更新方式。若干標準測試函數(shù)仿真和威氏(Wilcoxon)符號秩檢驗的結(jié)果顯示,AQPSO算法在處理多個局部最優(yōu)解相差較小時效果較好,而DARPSO算法在處理全局最優(yōu)解與局部最優(yōu)解相差較大的問題時效果較好。同時,在處理復雜高維函數(shù)的優(yōu)化問題上,本文提出的AQPSO算法、DARPSO算法,與QPSO算法、ARPSO算法以及PSO算法相比具有更好性能。
[Abstract]:Particle swarm optimization (Particle Swarm Optimization PSO) is a new swarm intelligence optimization algorithm, which has the characteristics of distributed, cooperative, self-organizing and simple implementation. This makes it possible for the algorithm to deal with all kinds of complex problems quickly when the global information is lacking, and also opens up a new way for solving typical complex problems. However, the algorithm is still likely to fall into local optimum when dealing with high dimensional complex problems. How to enhance the global search ability by ensuring the balance between Exploration and Exploitation is a hot and difficult point in this field. The PSO algorithm is improved from two aspects. One is the quantum behavior particle swarm optimization algorithm based on Sun Jun et al. (Quantum-behaved Particle Swarm Optimization QPSO),) an adaptive quantum behavior particle swarm optimization algorithm based on Takagi-Sugeno (TS) fuzzy reasoning (Adaptive Quantum-behaved Particle Swarm Optimization AQPSO),) is proposed. Particle swarm optimization algorithm is improved. Using the information of population distribution and exploration process, the algorithm dynamically adjusts the parameters of the algorithm and its iterative method by TS fuzzy reasoning, so as to ensure the population exploration in a larger space and reduce the probability of falling into local optimum. Secondly, based on the attractive and repulsive PSO (ARPSO) algorithm proposed by Riget et al., this paper proposes a dynamic algorithm to adjust the inertia weight, (Dynamic attractive and repulsive PSO DARPSO), which is not a simple linear decrement strategy. Instead, the inertia weight is adjusted dynamically according to whether the particle is contracted or expanded, and a new updating method of particle position is designed according to TS fuzzy reasoning. The simulation results of several standard test functions and the (Wilcoxon) sign rank test show that the AQPSO algorithm is effective in dealing with multiple local optimal solutions with small differences. The DARPSO algorithm is effective in solving the problem where the global optimal solution is different from the local optimal solution. At the same time, the AQPSO algorithm, DARPSO algorithm proposed in this paper have better performance than QPSO algorithm, ARPSO algorithm and PSO algorithm in dealing with the optimization problem of complex high-dimensional function.
【學位授予單位】：青島大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TP18
本文編號：2292953
[Abstract]:Particle swarm optimization (Particle Swarm Optimization PSO) is a new swarm intelligence optimization algorithm, which has the characteristics of distributed, cooperative, self-organizing and simple implementation. This makes it possible for the algorithm to deal with all kinds of complex problems quickly when the global information is lacking, and also opens up a new way for solving typical complex problems. However, the algorithm is still likely to fall into local optimum when dealing with high dimensional complex problems. How to enhance the global search ability by ensuring the balance between Exploration and Exploitation is a hot and difficult point in this field. The PSO algorithm is improved from two aspects. One is the quantum behavior particle swarm optimization algorithm based on Sun Jun et al. (Quantum-behaved Particle Swarm Optimization QPSO),) an adaptive quantum behavior particle swarm optimization algorithm based on Takagi-Sugeno (TS) fuzzy reasoning (Adaptive Quantum-behaved Particle Swarm Optimization AQPSO),) is proposed. Particle swarm optimization algorithm is improved. Using the information of population distribution and exploration process, the algorithm dynamically adjusts the parameters of the algorithm and its iterative method by TS fuzzy reasoning, so as to ensure the population exploration in a larger space and reduce the probability of falling into local optimum. Secondly, based on the attractive and repulsive PSO (ARPSO) algorithm proposed by Riget et al., this paper proposes a dynamic algorithm to adjust the inertia weight, (Dynamic attractive and repulsive PSO DARPSO), which is not a simple linear decrement strategy. Instead, the inertia weight is adjusted dynamically according to whether the particle is contracted or expanded, and a new updating method of particle position is designed according to TS fuzzy reasoning. The simulation results of several standard test functions and the (Wilcoxon) sign rank test show that the AQPSO algorithm is effective in dealing with multiple local optimal solutions with small differences. The DARPSO algorithm is effective in solving the problem where the global optimal solution is different from the local optimal solution. At the same time, the AQPSO algorithm, DARPSO algorithm proposed in this paper have better performance than QPSO algorithm, ARPSO algorithm and PSO algorithm in dealing with the optimization problem of complex high-dimensional function.
【學位授予單位】：青島大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TP18
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