本文選題：人工蜂群算法　切入點(diǎn)：跨維度搜索策略　出處：《計(jì)算機(jī)應(yīng)用》2017年03期

【摘要】：針對(duì)人工蜂群(ABC)算法不易跳出局部最優(yōu)解的缺點(diǎn),提出了多交互式人工蜂群(MIABC)算法。該算法在基本人工蜂群算法的基礎(chǔ)上引入隨機(jī)鄰域搜索策略,結(jié)合跨維搜索策略,且改進(jìn)蜜蜂越限處理方式,使得算法搜索方式多樣化,從而使得算法搜索更具跳躍性,不易陷入局部最優(yōu)解,同時(shí),對(duì)其進(jìn)行收斂性分析和性能測(cè)試。在五種經(jīng)典基準(zhǔn)測(cè)試函數(shù)和時(shí)間復(fù)雜度實(shí)驗(yàn)上的仿真結(jié)果表明,相對(duì)于標(biāo)準(zhǔn)人工蜂群算法和基本粒子群優(yōu)化(PSO)算法,該算法在1E-2精度下收斂速度提高了約30%和65%,搜索精度更優(yōu),且在高維求解問題方面有明顯優(yōu)勢(shì)。
[Abstract]:In view of the disadvantage of artificial bee colony (ABC) algorithm which is not easy to jump out of the local optimal solution, a multi-interactive artificial bee colony MIABC algorithm is proposed. Based on the basic artificial bee colony algorithm, the random neighborhood search strategy is introduced, and the cross-dimensional search strategy is combined. And improve the bee beyond the limit processing way, make the algorithm search way diversification, so that the algorithm search more jumping, not easy to fall into the local optimal solution, at the same time, The simulation results of five classical benchmark functions and time complexity experiments show that compared with the standard artificial bee colony algorithm and the basic particle swarm optimization (PSO) algorithm, The convergence speed of the algorithm is improved by about 30% and 65% under 1E-2 precision, the search accuracy is better, and the algorithm has obvious advantages in solving problems in high dimension.
【作者單位】： 上海電機(jī)學(xué)院電氣學(xué)院;
【基金】：上海市教委科研創(chuàng)新項(xiàng)目(13YZ140)~~
【分類號(hào)】：TP18

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