本文選題：動態(tài)搜索煙花算法　切入點：爆炸半徑　出處：《計算機科學(xué)與探索》2017年03期 　論文類型：期刊論文

【摘要】：采用核心煙花動態(tài)爆炸半徑策略的動態(tài)搜索煙花算法(dynamic search fireworks algorithm,dyn FWA)已被證明是解決優(yōu)化問題的一個重要算法。然而,dyn FWA的尋優(yōu)精度低且容易過早地陷入局部最優(yōu)解。為了改善上述的缺陷,通過嵌入一種利用歷史成功信息生成兩種不同的學(xué)習(xí)因子來改進傳統(tǒng)的動態(tài)搜索煙花算法,稱為改進的動態(tài)搜索煙花算法(improved dyn FWA,Idyn FWA)。算法中的學(xué)習(xí)因子充分利用搜索過程中每一代最好的煙花個體信息,使得煙花具有向群體的優(yōu)良搜索信息學(xué)習(xí)的能力,并且它的兩種不同產(chǎn)生方式有助于平衡算法的局部搜索和全局搜索能力。改進后的算法在CEC2013的28個Benchmark函數(shù)上進行測試,實驗結(jié)果表明Idyn FWA的尋優(yōu)效果明顯優(yōu)于dyn FWA,并且比粒子群算法SPSO2011和差分演化算法DE/randto-best/1能達到更好的尋優(yōu)性能。
[Abstract]:The dynamic search fireworks algorithm dyn FWAs (dynamic search fireworks algorithm) has been proved to be an important algorithm to solve the optimization problem. However, the optimization accuracy of FWA is low and it is easy to fall into the local optimal solution prematurely. In order to improve the above deficiencies, The traditional dynamic search fireworks algorithm is improved by embedding two different learning factors using historical success information. The algorithm is called improved dynamic search fireworks algorithm. The learning factors in the algorithm make full use of the best individual information of each generation in the search process, so that fireworks have the ability to learn from the excellent search information of the group. And its two different generation methods are helpful to balance the local search and global search ability of the algorithm. The improved algorithm is tested on 28 Benchmark functions of CEC2013. The experimental results show that the optimization performance of Idyn FWA is better than that of dyn FWA, and it is better than that of particle swarm optimization (SPSO2011) and differential evolution algorithm (DE/randto-best/1).
【作者單位】： 安徽大學(xué)計算機科學(xué)與技術(shù)學(xué)院;安徽大學(xué)計算智能與信號處理教育部重點實驗室;金陵科技學(xué)院計算機學(xué)院;
【基金】：國家自然科學(xué)基金No.61375121 安徽高校省級自然科學(xué)研究項目No.KJ2013A009 安徽大學(xué)博士啟動基金 金科院引進人才科研項目No.jit-rcyj-201505~~
【分類號】：TP18
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本文編號：1618704