本文選題：群體智能　切入點(diǎn)：果蠅優(yōu)化算法　出處：《湖南科技大學(xué)》2017年碩士論文　論文類型：學(xué)位論文

【摘要】：群體智能算法以其實(shí)現(xiàn)簡單靈活以及能有效解決高維、復(fù)雜的優(yōu)化問題等優(yōu)點(diǎn),在計(jì)算智能領(lǐng)域獲得了越來越多的關(guān)注。果蠅優(yōu)化算法是一種模擬果蠅覓食行為的群體智能優(yōu)化算法。本文結(jié)合云模型對(duì)果蠅優(yōu)化算法提出了一些改進(jìn)策略,以提高算法的收斂性能,并進(jìn)一步拓展到多目標(biāo)優(yōu)化和實(shí)際工程應(yīng)用。本文主要工作包括以下幾個(gè)方面。論文首先介紹了果蠅優(yōu)化算法的研究背景及意義,回顧和總結(jié)了果蠅優(yōu)化算法的國內(nèi)外研究現(xiàn)狀和進(jìn)展,對(duì)果蠅優(yōu)化算法的原理和步驟進(jìn)行了詳細(xì)的介紹,并給出了果蠅優(yōu)化算法的偽代碼。然后,對(duì)云模型的定義和正態(tài)云發(fā)生器的實(shí)現(xiàn)進(jìn)行了相關(guān)介紹。為了提高果蠅優(yōu)化算法的全局收斂能力和收斂精度,提出了一種基于云學(xué)習(xí)的雙態(tài)果蠅優(yōu)化算法。算法借鑒自然界群體分工的特性,在尋優(yōu)過程中將果蠅群體分為“搜索”和“捕食”兩種狀態(tài)的種群,平衡算法的全局搜索與局部開采能力。另外,利用云模型描述覓食過程中的隨機(jī)性和模糊性,增強(qiáng)逃離局部最優(yōu)的能力。利用23個(gè)Benchmarks測(cè)試函數(shù)對(duì)所提算法進(jìn)行了測(cè)試,實(shí)驗(yàn)結(jié)果表明,所提方法能顯著提高算法的全局收斂能力和收斂精度。鑒于基于濃度判定值計(jì)算的候選解產(chǎn)生機(jī)制存在易陷入早熟收斂以及不能優(yōu)化最優(yōu)解為負(fù)值的優(yōu)化問題,在新的候選解產(chǎn)生機(jī)制的基礎(chǔ)上結(jié)合正態(tài)云模型,提出了一種基于正態(tài)云模型的果蠅優(yōu)化算法。算法利用正態(tài)云模型對(duì)果蠅覓食過程中的隨機(jī)性和模糊性進(jìn)行描述,提高搜索效率。提出了一種正態(tài)云模型參數(shù)自適應(yīng)策略,使算法前期具有較強(qiáng)的全局收斂能力,后期擁有良好的收斂精度。利用33個(gè)Benchmarks測(cè)試函數(shù)對(duì)算法進(jìn)行了測(cè)試,實(shí)驗(yàn)結(jié)果表明,所提算法能獲得良好的收斂性能。結(jié)合Pareto占優(yōu)概念以及外部精英存檔策略,提出了一種基于云模型的多目標(biāo)果蠅優(yōu)化算法,將果蠅優(yōu)化算法拓展到多目標(biāo)問題的優(yōu)化。采用基于歸一化最近鄰域多樣性測(cè)量方法來保持非占優(yōu)解集的分布性和多樣性。利用WFG和CEC2009多目標(biāo)測(cè)試問題組對(duì)所提算法進(jìn)行了測(cè)試,實(shí)驗(yàn)結(jié)果表明,所提算法獲得的非占優(yōu)解集能夠較好的趨近于真實(shí)Pareto前沿,并且能保持良好的散布性。為了進(jìn)一步驗(yàn)證所提方法在實(shí)際的工程優(yōu)化設(shè)計(jì)中的有效性,將基于正態(tài)云模型的果蠅優(yōu)化算法應(yīng)用于永磁同步電機(jī)參數(shù)辨識(shí),將基于云模型的多目標(biāo)果蠅優(yōu)化算法應(yīng)用于飛行器熱導(dǎo)管參數(shù)優(yōu)化設(shè)計(jì)以及減速器優(yōu)化設(shè)計(jì)。實(shí)際應(yīng)用系統(tǒng)模型的實(shí)驗(yàn)結(jié)果證實(shí)了所提方法的有效性。
[Abstract]:Swarm intelligence algorithm has the advantages of simple and flexible implementation and can effectively solve high-dimensional and complex optimization problems. More and more attention has been paid in the field of computational intelligence. Drosophila optimization algorithm is a swarm intelligence optimization algorithm which simulates the foraging behavior of Drosophila melanogaster. In this paper, some improved strategies are proposed based on cloud model for Drosophila optimization algorithm. In order to improve the convergence performance of the algorithm, and further expand to multi-objective optimization and practical engineering applications. The main work of this paper includes the following aspects. Firstly, the background and significance of Drosophila optimization algorithm are introduced. This paper reviews and summarizes the research status and progress of Drosophila optimization algorithm at home and abroad, introduces the principle and steps of Drosophila optimization algorithm in detail, and gives the pseudo code of Drosophila optimization algorithm. The definition of cloud model and the implementation of normal cloud generator are introduced. In order to improve the global convergence ability and convergence accuracy of Drosophila optimization algorithm, A two-state optimization algorithm for Drosophila fly based on cloud learning is proposed. The algorithm uses the characteristics of natural population division for reference and divides Drosophila population into "searching" and "predatory" populations in the process of optimization. In addition, the cloud model is used to describe the randomness and fuzziness in the foraging process to enhance the ability to escape from the local optimum. 23 Benchmarks test functions are used to test the proposed algorithm. Experimental results show that the proposed method can significantly improve the global convergence ability and convergence accuracy of the algorithm. In view of the problem that the candidate solution generation mechanism based on the concentration decision value calculation is prone to premature convergence and can not optimize the optimal solution to negative value. Based on the new candidate solution generation mechanism, an optimization algorithm based on normal cloud model is proposed to describe the randomness and fuzziness of the foraging process of Drosophila melanogaster. In order to improve search efficiency, a parameter adaptive strategy for normal cloud model is proposed, which makes the algorithm have strong global convergence ability in the early stage and good convergence accuracy in the latter stage. 33 Benchmarks test functions are used to test the algorithm. Experimental results show that the proposed algorithm can achieve good convergence performance. Combined with the concept of Pareto dominance and the external elite archiving strategy, a multi-objective Drosophila optimization algorithm based on cloud model is proposed. The optimization algorithm of Drosophila is extended to the optimization of multi-objective problem. Based on the normalized nearest neighborhood diversity measurement method, the distribution and diversity of non-dominant solution sets are maintained. WFG and CEC2009 multiobjective test problem groups are used to solve the problem. The algorithm is tested, The experimental results show that the set of non-dominant solutions obtained by the proposed algorithm can reach the real Pareto front and maintain good dispersion. In order to further verify the effectiveness of the proposed method in the practical engineering optimization design. The optimal algorithm of Drosophila based on normal cloud model is applied to parameter identification of permanent magnet synchronous motor (PMSM). The multi-objective Drosophila optimization algorithm based on the cloud model is applied to the optimization design of the thermal duct parameters and the reducer of the aircraft. The experimental results of the practical application system model show the effectiveness of the proposed method.
【學(xué)位授予單位】：湖南科技大學(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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