【摘要】：針對(duì)差分進(jìn)化算法求解組合優(yōu)化問(wèn)題存在的局限性,引入計(jì)算機(jī)語(yǔ)言中的2種按位運(yùn)算符,對(duì)差分進(jìn)化算法的變異算子進(jìn)行重新設(shè)計(jì),用來(lái)求解不確定需求和旅行時(shí)間下同時(shí)取貨和送貨的隨機(jī)車輛路徑問(wèn)題(SVRPSPD)。通過(guò)對(duì)車輛路徑問(wèn)題的benchmark問(wèn)題和SVRPSPD問(wèn)題進(jìn)行路徑優(yōu)化,并同差分進(jìn)化算法和遺傳算法的計(jì)算結(jié)果進(jìn)行比較,驗(yàn)證了離散差分進(jìn)化算法的性能。結(jié)果表明,離散差分進(jìn)化算法在解決復(fù)雜的SVRPSPD問(wèn)題時(shí),具有較好的優(yōu)化性能,不僅能得到更好的優(yōu)化結(jié)果,而且具有更快的收斂速度。
[Abstract]:In view of the limitations of differential evolution algorithm for solving combinatorial optimization problems, 2 kinds of bit operators in computer language are introduced, and the mutation operators of differential evolution algorithm are redesigned to solve the random vehicle routing problem (SVRPSPD) for the simultaneous delivery and delivery of goods and goods under uncertain demand and travel time. The benchmark problem and the SVRPSPD problem are optimized and compared with the results of the differential evolution and genetic algorithms. The performance of the discrete differential evolution algorithm is verified. The results show that the discrete differential evolution algorithm has better optimization performance in solving complex SVRPSPD problems and not only can get better optimization results. And it has a faster rate of convergence.
【作者單位】： 天津師范大學(xué)管理學(xué)院;北京航空航天大學(xué)經(jīng)濟(jì)管理學(xué)院;
【基金】：國(guó)家自然科學(xué)基金資助項(xiàng)目(71071008) 天津市2012年度哲學(xué)社會(huì)科學(xué)研究規(guī)劃項(xiàng)目(TJGL12-079)
【分類號(hào)】：U492.22;TP18

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本文編號(hào)：2123627