本文關鍵詞：動態(tài)搜索空間策略下的粒子群算法改進及其拓展研究　出處：《江西理工大學》2017年碩士論文　論文類型：學位論文

  更多相關文章： 群體智能 搜索空間 逐層演化 早熟

【摘要】：隨著以粒子群為例的群智能算法在各領域內愈發(fā)廣泛的使用,其算法后期早熟以及最終解精度不高等現象成了務須重視并嘗試解決的問題。本文以粒子群算法為切入點,通過觀察粒子在搜索過程中具體空間特性,逐步改進并擴展優(yōu)化策略,最終構建出具有一定廣泛適用性的優(yōu)化策略。具體主要包括以下三方面:(1)為進一步研究和優(yōu)化粒子群算法,在采用非線性學習因子的同時,提出了一種新的牽引策略來共同優(yōu)化粒子群算法(Particle Swarm Optimization Algorithm based on Homing HMPSO)。該策略通過使粒子發(fā)生偏移于最優(yōu)解的位移,增加粒子活性,從而提升算法后期的尋優(yōu)能力。依實驗需求將各基準函數進行調整變換并通過仿真實驗進行尋優(yōu)測試。結果表明,在算法后期的尋優(yōu)能力有明顯提升,且具有較好的魯棒性。最后,估算出算法尋優(yōu)結果精度高于指定閥值精度的概率區(qū)間,證明該策略具有良好可信度。(2)為進一步緩解粒子群優(yōu)化算法在其后期收斂速度慢、早熟等問題,提出了一種掛載式的、依賴自適應閥值和已知全局最優(yōu)解的壓縮搜索空間策略。并在此基礎上對粒子重新分配初始位置、調整速度權值來提升算法的后期探索能力。實驗表明,在使用相同的權重和學習因子策略時,比之原粒子群優(yōu)化算法具有較好的表現,在對量子粒子群算法進行嵌入時依然具有一定效果。該策略可以有效避免早熟問題,提升算法在后期的尋優(yōu)效果,具有較好的魯棒性。(3)群體智能算法的主要任務便是在有限的時間內盡可能的獲得精度更高的解。但由于早熟等常見問題,使得一個精度更高的解需要通過提供額外的迭代次數來取得。為能徹底解決早熟問題的同時保持原算法主體不變且可與現有優(yōu)化理論協(xié)同優(yōu)化,在前期仿真實驗和理論證明的基礎上提出了一種逐層演化的改進策略。利用在原算法中構建基于搜索空間壓縮理論的自適應系統(tǒng),通過逐層的壓縮、選擇、再初始化的操作,以包括壓縮后搜索空間在內的社會信息作為遺傳知識,指導尋優(yōu)過程,從而實現最終解精度的提升、避免早熟問題的出現。對基準函數進行仿真實驗可以看出該策略在提升算法精度,增強后期個體活性方面具有良好的表現。上述三個策略,依次證實了:提升種群多樣性有助于提升粒子群算法最終表現;在同等情況下,壓縮搜索空間可以使得算法最終表現得到提升;逐層的演化策略作為在種群多樣性與搜索空間二者的基礎上構建的優(yōu)化策略較之于前者具有更好的普適性。
[Abstract]:With particle swarm optimization as an example, swarm intelligence algorithm is more and more widely used in various fields. In this paper, particle swarm optimization (PSO) is taken as the starting point, and the specific spatial characteristics of particles in the search process are observed. Gradually improve and expand the optimization strategy, and finally build an optimization strategy with a wide range of applicability, including the following three aspects: 1) for further research and optimization of particle swarm optimization algorithm. The nonlinear learning factor is used at the same time. A new traction strategy is proposed to optimize particle swarm optimization (PSO). Particle Swarm Optimization Algorithm based on Homing HMPSO). . this strategy shifts the particle to the optimal solution by causing the particle to shift to the optimal solution. Increase particle activity, thus improve the ability of optimization in the later stage of the algorithm. According to the requirements of the experiment, the benchmark functions are adjusted and transformed, and the optimization tests are carried out through simulation experiments. The results show that. In the later stage of the algorithm, the optimization ability is obviously improved, and the algorithm has good robustness. Finally, the probability interval of the accuracy of the algorithm is estimated to be higher than the specified threshold precision. It is proved that the strategy has good reliability.) in order to further alleviate the problems of slow convergence rate and premature convergence of particle swarm optimization algorithm, a mount formula is proposed. The search space strategy depends on adaptive threshold and known global optimal solution. On this basis, the initial position of particle is reassigned and the velocity weight is adjusted to improve the ability of the algorithm to explore in the later stage. When using the same weight and learning factor strategy, it has better performance than the original particle swarm optimization algorithm. This strategy can effectively avoid the premature problem and improve the optimization effect of the algorithm in the later stage. The main task of swarm intelligence algorithm is to get more accurate solution in limited time. However, due to the common problems such as precocity and so on. In order to solve the precocious problem completely and keep the main body of the original algorithm unchanged and cooperate with the existing optimization theory, a more accurate solution needs to be obtained by providing additional iterations. On the basis of previous simulation experiments and theoretical proof, an improved strategy of hierarchical evolution is proposed. An adaptive system based on search space compression theory is constructed in the original algorithm. Reinitialize the operation, including the compressed search space, including social information as genetic knowledge, to guide the optimization process, so as to achieve the final solution accuracy. To avoid the problem of precocity. The simulation of benchmark function shows that the strategy has a good performance in improving the algorithm accuracy and enhancing the individual activity in the later stage. The three strategies mentioned above. It is proved in turn that improving the diversity of population is helpful to the final performance of PSO. In the same situation, the algorithm can be improved by compressing the search space. As an optimization strategy based on population diversity and search space, the evolutionary strategy of layer by layer has better universality than the former.
【學位授予單位】：江西理工大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TP18

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