【摘要】：磷蝦群優(yōu)化算法是由Gandomi和Alavi于2012年提出的一種新的算法.該算法具有收斂性強(qiáng)、編程簡(jiǎn)單和易于實(shí)現(xiàn)等優(yōu)點(diǎn),也具有收斂精度較差、計(jì)算效率較低和應(yīng)用領(lǐng)域較少等缺點(diǎn).本文對(duì)磷蝦群優(yōu)化算法進(jìn)行了改進(jìn)和應(yīng)用,主要工作如下:1.提出了一種基于自然選擇和隨機(jī)擾動(dòng)的改進(jìn)磷蝦群優(yōu)化算法.在磷蝦群優(yōu)化算法的每一輪迭代中,對(duì)磷蝦個(gè)體運(yùn)動(dòng)中的誘導(dǎo)權(quán)重和覓食權(quán)重采用基于時(shí)變的非線性遞減策略,其次在新一代磷蝦個(gè)體的生成過(guò)程中加入隨機(jī)擾動(dòng),最后通過(guò)自然選擇,提升新一代磷蝦個(gè)體的質(zhì)量,以此來(lái)提升算法的全局搜索和局部勘探能力.2.提出了一種基于改進(jìn)的粒子群和磷蝦群的混合算法.該算法首先對(duì)磷蝦群優(yōu)化算法中的覓食權(quán)重和誘導(dǎo)權(quán)重采用了一種新的非線性遞減策略,然后將其與慣性權(quán)重指數(shù)遞減的粒子群算法混合,采用雙子種群策略共享有效信息,以此來(lái)提升運(yùn)行效率.最后將自然界中生物優(yōu)勝劣汰的進(jìn)化機(jī)制引入.結(jié)果表明,求解精度和運(yùn)行效率都得到了很大的提升.3.將上述用于解決無(wú)約束優(yōu)化問(wèn)題的基于改進(jìn)的粒子群和磷蝦群的混合算法在加入約束處理機(jī)制后應(yīng)用于求解約束優(yōu)化問(wèn)題——非線性混合整數(shù)規(guī)劃問(wèn)題,并在求解精度和成功率上與其他算法進(jìn)行了比較.
[Abstract]:Krill swarm optimization algorithm is a new algorithm proposed by Gandomi and Alavi in 2012. The algorithm has the advantages of strong convergence, simple programming and easy implementation, but also has the disadvantages of poor convergence accuracy, low computational efficiency and less application fields. In this paper, the optimization algorithm of krill colony is improved and applied, the main work is as follows: 1. An improved algorithm based on natural selection and random perturbation is proposed. In each iteration of the algorithm, the induced weight and the foraging weight in the individual movement of the krill are reduced by the nonlinear decreasing strategy based on time varying, and then the random disturbance is added to the process of the generation of the new generation of individuals of the krill. Finally, through natural selection, the quality of the new generation of krill individuals is improved to improve the global search and local exploration ability of the algorithm. A hybrid algorithm based on improved particle swarm and krill swarm is proposed. In this algorithm, a new nonlinear decreasing strategy is adopted for the feeding weight and induced weight of the population optimization algorithm of krill population, and then the algorithm is mixed with the particle swarm optimization algorithm with decreasing inertial weight index, and the effective information is shared by using the Gemini population strategy. In order to improve the operational efficiency. Finally, the evolutionary mechanism of survival of the fittest in nature is introduced. The results show that the accuracy and efficiency of the solution are greatly improved. The hybrid algorithm based on improved particle swarm and krill swarm for solving the unconstrained optimization problem is applied to the constrained optimization problem-nonlinear mixed integer programming problem after the constraint processing mechanism is added. The accuracy and success rate are compared with other algorithms.
【學(xué)位授予單位】：北方民族大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類(lèi)號(hào)】：TP18

【參考文獻(xiàn)】

相關(guān)期刊論文 前3條

1 王磊;張漢鵬;;基于混沌搜索與精英交叉算子的磷蝦覓食算法[J];計(jì)算機(jī)工程;2015年03期

2 白俊強(qiáng);尹戈玲;孫智偉;;基于二階振蕩及自然選擇的隨機(jī)權(quán)重混合粒子群算法[J];控制與決策;2012年10期

3 李會(huì)榮;高岳林;李濟(jì)民;;一種非線性遞減慣性權(quán)重策略的粒子群優(yōu)化算法[J];商洛學(xué)院學(xué)報(bào);2007年04期

相關(guān)博士學(xué)位論文 前1條

1 王維博;粒子群優(yōu)化算法研究及其應(yīng)用[D];西南交通大學(xué);2012年

，

本文編號(hào)：2141182