本文選題：改進(jìn)大紅斑蝶優(yōu)化算法(IMBO) + 改進(jìn)離子運(yùn)動(dòng)算法(IIMO)��； 參考：《廣西民族大學(xué)》2017年碩士論文

【摘要】：大紅斑蝶優(yōu)化算法(MBO)和離子運(yùn)動(dòng)算法(IMO)均為2015年新提出的群智能隨機(jī)優(yōu)化算法。然而這兩種算法仍存在局部搜索能力不強(qiáng)、優(yōu)化精度不高、早熟收斂等不足,算法的理論基礎(chǔ)也還不完善�；谶@一情況,本論文就如何改進(jìn)這兩種算法的優(yōu)化性能展開研究。本論文的主要研究成果如下:(1)針對(duì)大紅斑蝶優(yōu)化算法仍存在全局搜索能力不強(qiáng)、收斂速度慢、易陷入局部極值之不足,提出一種采用動(dòng)態(tài)分割種群策略的改進(jìn)MBO算法。該算法采用將群體動(dòng)態(tài)隨機(jī)分割成兩個(gè)子群體的策略,不同子群中的大紅斑蝶采用不同的搜索方法,以保持種群搜索的多樣性。實(shí)驗(yàn)結(jié)果表明,改進(jìn)后的MBO算法的全局搜索能力有了明顯地提高,在函數(shù)優(yōu)化中具有更好的收斂速度及優(yōu)化精度。(2)提出一種解決多目標(biāo)優(yōu)化問題的MOIMBO。實(shí)驗(yàn)結(jié)果表明,該算法解決多目標(biāo)優(yōu)化問題的平均性能均優(yōu)于PSO及MBO算法。(3)為了克服離子運(yùn)動(dòng)算法(IMO)存在之不足,提出一種新的改進(jìn)離子運(yùn)動(dòng)算法(IIMO)。該IIMO算法基于同類離子相互排斥而異類離子相互吸引、以及離子在液態(tài)空間中出現(xiàn)隨機(jī)移動(dòng)的特征,刻畫出一種新的離子運(yùn)動(dòng)數(shù)學(xué)模型。實(shí)驗(yàn)結(jié)果表明:IIMO算法比IMO和PSO具有更快的收斂速度、更強(qiáng)的局部搜索能力和全局搜索能力,IIMO算法的魯棒性比IMO算法和PSO算法強(qiáng)。
[Abstract]:Both MBOs and IMO are new swarm intelligence stochastic optimization algorithms proposed in 2015. However, the two algorithms still have some shortcomings, such as weak local search ability, low optimization accuracy, premature convergence and so on, and the theoretical basis of the algorithm is not perfect. Based on this situation, this paper studies how to improve the optimization performance of these two algorithms. The main research results of this paper are as follows: (1) aiming at the deficiency of global search ability, slow convergence rate and easy to fall into local extremum in the algorithm, an improved MBO algorithm based on dynamic population segmentation strategy is proposed. The algorithm adopts the strategy of randomly dividing the population into two subpopulations, and the different search methods are used by the butterflies in different subgroups to keep the diversity of the population search. The experimental results show that the global search ability of the improved MBO algorithm is obviously improved, and the improved MBO algorithm has better convergence speed and optimization precision in function optimization. The experimental results show that the average performance of the proposed algorithm is better than that of PSO and MBO algorithms. In order to overcome the shortcomings of ion motion algorithm (IMO), a new improved ion motion algorithm (IIMO) is proposed. The IIMO algorithm describes a new mathematical model of ion motion based on the characteristics of the similar ions repel each other and the heterogeneous ions attract each other and the ions move randomly in the liquid space. The experimental results show that the ratio IIMO algorithm has faster convergence speed and stronger local and global search ability than IMO and PSO. The robustness of IIMO algorithm is better than that of IMO algorithm and PSO algorithm.
【學(xué)位授予單位】：廣西民族大學(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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本文編號(hào)：2019397