【摘要】：鴿群優(yōu)化算法是新的啟發(fā)式算法,是由段海濱教授等人于2014年首次提出。鴿群算法的思想是模擬鴿群利用地球磁場和地標組合來歸巢的過程。鴿群算法具有原理相對簡單、所需調整的參數較少、比較容易實現等特點。還有計算相對簡單,魯棒性較強等顯著優(yōu)勢,相對于其他部分算法而言還有收斂速度較快的優(yōu)勢。與此同時鴿群優(yōu)化算法還存在不足之處,該算法有收斂精度偏低,容易出現局部最優(yōu)的情況,穩(wěn)定性較差等缺點。因此,鴿群優(yōu)化算法在理論方面和應用方面,是有待于更深入的研究和更廣泛的擴展。本文針對鴿群優(yōu)化算法所存在的不足進行了分析,從添加收斂因子、位置因子、速度因子和子群變異策略等方面對鴿群算法進行了改進,還將改進后的算法應用到實際的優(yōu)化問題當中。所涉及的主要工作內容將總結為以下3個方面:(1)采用添加收斂因子、增加位置因子和速度因子等策略對鴿群算法進行改進。不僅增強鴿子的飛行活力,提高鴿子種群的多樣性,并且能夠有效的避免鴿群的早熟收斂現象,使鴿群優(yōu)化算法具有一定的競爭力。并且完成了改進后的鴿群優(yōu)化算法的相關標準函數優(yōu)化的測試。(2)通過添加子群變異策略對鴿群優(yōu)化算法進行改進,將子群變異策略的思想應用到鴿群優(yōu)化算法中,克服了鴿群優(yōu)化算法容易早熟收斂情況,還增大了鴿子種群潛在的搜索空間。為了增強鴿群優(yōu)化算法的局部搜索能力,還對貪心策略進行引入,并且將改進后的鴿群優(yōu)化算法應用于0-1背包問題的求解。(3)通過把鴿群優(yōu)化算法和模擬退火算法進行互補融合。結合后的算法不僅僅具有鴿群算法的特點,還可以根據概率性進行劣向的轉移,并且以一定的概率去接受劣解,可以讓鴿群優(yōu)化算法跳出局部最優(yōu)解的情況,從而達到實現全局最優(yōu)的目的。在與算法融合的基礎上還對鴿群優(yōu)化算法引入自適應溫度衰變系數,可以根據當前的環(huán)境自動調整搜索條件,來達到提高搜索效率的目的。本章還將改進后的鴿群算法應用于求解無人潛水器路徑規(guī)劃的問題當中,以此來增加改進后的鴿群算法的應用范圍,同時也表明了算法的有效性和可行性。
[Abstract]:Pigeon swarm optimization, a new heuristic algorithm, was first proposed by Professor Duan Haibin in 2014. The idea of pigeon swarm algorithm is to simulate the homing process of pigeon swarm using the combination of geomagnetic field and landmarks. Pigeon swarm algorithm has the advantages of relatively simple principle, few parameters to be adjusted and easy to implement. There are also obvious advantages such as relatively simple computation and strong robustness, and the advantages of faster convergence rate compared with other algorithms. At the same time, the pigeon swarm optimization algorithm has some shortcomings, such as low convergence accuracy, local optimum and poor stability. Therefore, pigeon swarm optimization algorithm needs to be further studied and extended in theory and application. This paper analyzes the shortcomings of pigeon swarm optimization algorithm and improves the algorithm from the aspects of adding convergence factor, position factor, speed factor and subgroup mutation strategy. The improved algorithm is also applied to practical optimization problems. The main work involved will be summarized as follows: (1) improving pigeon swarm algorithm by adding convergence factor, increasing position factor and speed factor. It can not only enhance the flight vitality of pigeons, improve the diversity of pigeon population, but also effectively avoid the phenomenon of premature convergence of pigeon population, so that the pigeon swarm optimization algorithm has certain competitiveness. And completed the improved pigeon swarm optimization algorithm related standard function optimization test. (2) by adding subgroup mutation strategy to improve the pigeon swarm optimization algorithm, the idea of subgroup mutation strategy is applied to pigeon swarm optimization algorithm. It overcomes the premature convergence of pigeon swarm optimization algorithm and increases the potential search space of pigeon population. In order to enhance the local search ability of pigeon swarm optimization algorithm, the greedy strategy is also introduced, and the improved pigeon swarm optimization algorithm is applied to solve the 0-1 knapsack problem. (3) the pigeon swarm optimization algorithm is combined with simulated annealing algorithm to solve the 0-1 knapsack problem. The combined algorithm not only has the characteristics of pigeon swarm algorithm, but also can transfer the bad solution according to the probability, and accept the inferior solution with a certain probability, so that the pigeon swarm optimization algorithm can jump out of the local optimal solution. In order to achieve the goal of global optimization. Based on the fusion with the algorithm, the adaptive temperature decay coefficient is introduced to the pigeon swarm optimization algorithm, which can automatically adjust the search conditions according to the current environment to achieve the purpose of improving the search efficiency. In this chapter, the improved pigeon swarm algorithm is applied to solve the path planning problem of unmanned submersible vehicle, so as to increase the scope of application of the improved pigeon swarm algorithm, and also show the effectiveness and feasibility of the algorithm.
【學位授予單位】：廣西民族大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TP18

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