本文選題：第Ⅰ類裝配線平衡問題　切入點(diǎn)：多目標(biāo)優(yōu)化　出處：《科技通報(bào)》2017年11期 　論文類型：期刊論文

【摘要】：為求解多目標(biāo)第Ⅰ類裝配線平衡問題(MOABLP-Ⅰ),提出了一種改進(jìn)的差分進(jìn)化算法(IDEA)。該算法優(yōu)化目標(biāo)包括最優(yōu)工位數(shù),線生產(chǎn)效率和工位載荷波動(dòng)。采用基于優(yōu)先權(quán)的編碼方法使得個(gè)體解碼后總滿足裝配線約束關(guān)系,設(shè)計(jì)了自適應(yīng)雙變異策略和新型交叉操作算子使算法適應(yīng)離散優(yōu)化問題,引入"精英保留"機(jī)制增強(qiáng)算法逃離局部最優(yōu)的能力。通過(guò)測(cè)試問題集的驗(yàn)證,并比較了基本差分進(jìn)化算法和離散型差分進(jìn)化算法,結(jié)果表明IDEA在求解大規(guī)模MOABLP-Ⅰ上質(zhì)量最優(yōu)。
[Abstract]:In order to solve the multiobjective class I assembly line balance problem, an improved differential evolutionary algorithm (DEA) is proposed. The optimization goal of the algorithm includes the optimal number of stations. The efficiency of line production and load fluctuation of work station are fluctuated. The coding method based on priority is used to make the individual satisfy the assembly line constraint relationship after decoding. Adaptive double mutation strategy and new crossover operator are designed to adapt the algorithm to discrete optimization problem. The "elite reservation" mechanism is introduced to enhance the ability of the algorithm to escape from the local optimum. The basic differential evolution algorithm and the discrete differential evolution algorithm are compared by the test problem set. The results show that IDEA is the best in solving large-scale MOABLP- 鈪，

本文編號(hào)：1571484