【摘要】：針對(duì)微粒群優(yōu)化算法易陷入局部最優(yōu)、出現(xiàn)早熟等不足,從作用力規(guī)則和種群拓?fù)浣Y(jié)構(gòu)兩方面進(jìn)行研究。提出一種混合作用力微粒群優(yōu)化(Hybrid force PSO,HFPSO)算法,將算法的搜索過(guò)程劃分為前期和后期兩個(gè)階段,分別構(gòu)造引斥力規(guī)則和雙引力規(guī)則,使算法搜索前期具有良好種群多樣性、搜索后期有較高尋優(yōu)精度。進(jìn)一步將生物趨利避害的行為選擇機(jī)制融入HFPSO算法,提出有向動(dòng)態(tài)拓?fù)浠旌献饔昧ξ⒘Ｈ簝?yōu)化算法,賦予微粒主觀能動(dòng)性使其靠近適應(yīng)值較好微粒、遠(yuǎn)離適應(yīng)值較差微粒,提出適應(yīng)值驅(qū)動(dòng)邊變化的有向動(dòng)態(tài)拓?fù)?Fitness-driven edge-changing unidirectional dynamic topology,FEUDT)結(jié)構(gòu),并將FEUDT結(jié)構(gòu)與HFPSO算法以結(jié)構(gòu)演化和算法進(jìn)化同步進(jìn)行的方式結(jié)合,進(jìn)一步提升算法的優(yōu)化性能。利用Benchmark函數(shù)對(duì)所提算法與標(biāo)準(zhǔn)PSO、搜索后期斥力增強(qiáng)型混合引斥力微粒群優(yōu)化(LRPSO)算法進(jìn)行性能對(duì)比測(cè)試,結(jié)果表明,所提算法具有較好的尋優(yōu)能力和較快的收斂速度。通過(guò)橋式系統(tǒng)可靠性優(yōu)化實(shí)例和供應(yīng)商參與的某汽車產(chǎn)品子系統(tǒng)可靠性設(shè)計(jì)優(yōu)化實(shí)例,驗(yàn)證了所提算法求解實(shí)際復(fù)雜優(yōu)化問題的有效性。
[Abstract]:The particle swarm optimization (PSO) algorithm is easy to fall into local optimum and premature, so it is studied from two aspects: force rules and population topology. A hybrid force particle swarm optimization (Hybrid force PSO-HFPSO) algorithm is proposed. The search process of the algorithm is divided into two stages: the early stage and the later stage. The repulsive force rule and the double gravity rule are constructed, respectively, so that the algorithm has good population diversity in the early stage of search. In the later stage of searching, the accuracy of searching is high. Furthermore, the behavior selection mechanism of biological convergence and avoidance is incorporated into the HFPSO algorithm, and a hybrid force particle swarm optimization algorithm with directed dynamic topology is proposed, which gives the particle subjective initiative to make it close to the better adaptive value and away from the poor adaptive particle. A novel adaptive edge driven oriented dynamic topology (Fitness-driven edge-changing unidirectional dynamic topology FEUDT) structure is proposed, and the FEUDT structure is combined with the HFPSO algorithm in the way of structure evolution and algorithm evolution synchronization to further improve the optimization performance of the algorithm. The Benchmark function is used to compare the performance of the proposed algorithm with that of the standard PSOs, and the performance of the (LRPSO) algorithm is compared with that of the (LRPSO) algorithm. The results show that the proposed algorithm has better optimization ability and faster convergence speed. The effectiveness of the proposed algorithm for solving the complex optimization problem is verified by the reliability optimization examples of the bridge system and the reliability design of a vehicle product subsystem with the participation of the supplier.
【作者單位】： 燕山大學(xué)河北省工業(yè)計(jì)算機(jī)控制工程重點(diǎn)實(shí)驗(yàn)室;燕山大學(xué)河北省重型機(jī)械流體動(dòng)力傳輸與控制重點(diǎn)實(shí)驗(yàn)室;先進(jìn)鍛壓成型技術(shù)與科學(xué)教育部重點(diǎn)實(shí)驗(yàn)室(燕山大學(xué));
【基金】：國(guó)家自然科學(xué)基金(51405426,51675460) 河北省自然科學(xué)基金(E2016203306)資助項(xiàng)目
【分類號(hào)】：TB114.3;TP18

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