本文關(guān)鍵詞：差分混合蛙跳算法的改進(jìn)及其應(yīng)用　出處：《廣東工業(yè)大學(xué)》2017年碩士論文　論文類型：學(xué)位論文

  更多相關(guān)文章： 混合蛙跳算法 差分進(jìn)化算法 歸檔集 越界處理 車輛路徑問(wèn)題

【摘要】：計(jì)算智能優(yōu)化算法是對(duì)自然界智慧和人類智慧的模仿,因其智能性、并行性和健壯性,具有很好的自適應(yīng)能力和很強(qiáng)的全局搜索能力,得到眾多研究者的廣泛關(guān)注.混合蛙跳算法(SFLA)是一種新興的智能優(yōu)化算法,該算法結(jié)合了模因算法的局部啟發(fā)式搜索和粒子群優(yōu)化算法的全局搜索兩者的優(yōu)點(diǎn),在進(jìn)化過(guò)程中先進(jìn)行局部精確搜索,再利用子群個(gè)體間的信息共享進(jìn)行全局搜索,兩者相互結(jié)合直至找出全局最優(yōu)解.SFLA結(jié)構(gòu)簡(jiǎn)單容易理解、控制參數(shù)少,具有很強(qiáng)的全局搜索能力.差分進(jìn)化算法也是一種新興的全局優(yōu)化算法,局部更新策略類似于遺傳算法,采用差分變異操作、交叉操作和選擇操作更新產(chǎn)生新個(gè)體.經(jīng)過(guò)一代代反復(fù)不斷的局部進(jìn)化,算法的搜索方向慢慢向全局最優(yōu)解的方向靠近.DE算法具有精確的局部搜索能力,魯棒性較強(qiáng),已成為智能優(yōu)化算法的重要分支.目前,將差分進(jìn)化算法的局部更新策略與其他優(yōu)化技術(shù)相結(jié)合來(lái)提高算法的優(yōu)化性能,已被廣泛應(yīng)用于各個(gè)領(lǐng)域,在科學(xué)研究和生產(chǎn)實(shí)踐中發(fā)揮著重要的作用.本文針對(duì)混合蛙跳算法在尋優(yōu)過(guò)程中易陷入局部最優(yōu)和早熟收斂的缺點(diǎn),利用差分進(jìn)化算法的局部精確搜索的特點(diǎn)和蛙跳算法強(qiáng)大的全局搜索能力融合提出一種改進(jìn)的差分蛙跳算法(DSFLA).該算法借鑒差分進(jìn)化中的變異交叉思想,在前期利用子群中其他個(gè)體的有用信息來(lái)更新最差個(gè)體,增加局部擾動(dòng)性,以提高種群的多樣性;在后期為加快收斂速度使用最好個(gè)體的信息進(jìn)行變異交叉操作.同時(shí)在每一次產(chǎn)生新個(gè)體后,都要進(jìn)行改進(jìn)的越界處理來(lái)動(dòng)態(tài)調(diào)整變化尺度,再與子群最差個(gè)體進(jìn)行選擇操作選出適應(yīng)值更優(yōu)的個(gè)體.本文還使用歸檔集進(jìn)一步保留種群的多樣性.通過(guò)對(duì)五個(gè)典型的連續(xù)優(yōu)化函數(shù)進(jìn)行實(shí)驗(yàn)仿真,測(cè)試結(jié)果表明DSFLA無(wú)論是在求最優(yōu)解的穩(wěn)定性上還是質(zhì)量上都明顯勝于SFLA和SFLA-AV,在前期保持種群多樣性和后期提高收斂速度避免算法早熟都起到了較好的效果.最后,本文將改進(jìn)的DSFLA運(yùn)用在物流中求解帶容量約束的車輛路徑優(yōu)化問(wèn)題上,采用實(shí)數(shù)編碼方式初始化種群,利用DEB規(guī)則處理約束問(wèn)題,實(shí)驗(yàn)仿真得到多種優(yōu)化路徑,可為實(shí)際物流問(wèn)題提供多種調(diào)度方案.
[Abstract]:Computational intelligence optimization algorithm is an imitation of natural intelligence and human intelligence, because of its intelligence, parallelism and robustness, it has good adaptive ability and strong global search ability. The hybrid leapfrog algorithm (SFLAs) is a new intelligent optimization algorithm. This algorithm combines the advantages of local heuristic search of meme algorithm and global search of particle swarm optimization algorithm. Then the information sharing among subgroups is used for global search, and the two are combined to find the global optimal solution. SFLA structure is simple and easy to understand, and the control parameters are less. Differential evolution algorithm is also a new global optimization algorithm, local update strategy is similar to genetic algorithm, using differential mutation operation. Crossover operations and selection operations update to produce new individuals. After generations of repeated local evolution, the search direction of the algorithm slowly to the direction of the global optimal solution close to the .DE algorithm has an accurate local search ability. Because of its strong robustness, it has become an important branch of intelligent optimization algorithm. At present, the local update strategy of differential evolution algorithm is combined with other optimization techniques to improve the optimization performance of the algorithm. It has been widely used in various fields and plays an important role in scientific research and production practice. This paper aims at the shortcomings of hybrid leapfrog algorithm which is prone to fall into local optimum and premature convergence in the process of optimization. Using the characteristic of local exact search of differential evolution algorithm and the powerful global search ability fusion of leapfrog algorithm, an improved differential leapfrog algorithm (DSFLAs) is proposed. The algorithm draws lessons from the idea of mutation crossover in differential evolution. In the early stage, the useful information of other individuals in the subgroup can be used to update the worst individual and increase the local disturbance, so as to improve the diversity of the population. In order to speed up the convergence of the best individual information mutation crossover operation. At the same time after each generation of new individuals must be improved cross-border processing to dynamically adjust the scale of change. Then the selection operation with the worst individuals of the subgroup is carried out to select the individuals with better fitness. The archival set is also used to further preserve the diversity of the population. Five typical continuous optimization functions are simulated experimentally. The test results show that DSFLA is superior to SFLA and SFLA-AV in the stability and quality of the optimal solution. Maintaining population diversity in the early stage and increasing convergence rate in the later stage to avoid premature algorithm have played a good effect. Finally. In this paper, the improved DSFLA is used to solve the vehicle routing optimization problem with capacity constraints in logistics, the real coding method is used to initialize the population, and the DEB rule is used to deal with the constraint problem. Simulation results show that many optimal paths can be obtained, which can provide a variety of scheduling schemes for practical logistics problems.
【學(xué)位授予單位】：廣東工業(yè)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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