本文選題：多目標(biāo)柔性作業(yè)車間調(diào)度 + Pareto熵�。� 參考：《小型微型計(jì)算機(jī)系統(tǒng)》2017年12期

【摘要】：針對(duì)多目標(biāo)柔性作業(yè)車間調(diào)度問(wèn)題,提出基于Pareto熵的雞群算法.首先,建立了多目標(biāo)柔性作業(yè)車間調(diào)度模型,優(yōu)化目標(biāo)為最大完工時(shí)間、最大機(jī)器負(fù)荷和所有機(jī)器總負(fù)荷.其次,將Pareto熵的概念引入雞群算法,通過(guò)計(jì)算Pareto前端的熵值和差熵值判斷目前種群的進(jìn)化狀態(tài),動(dòng)態(tài)調(diào)節(jié)慣性權(quán)重,使得調(diào)節(jié)過(guò)程具有針對(duì)性和目的性,同時(shí)為了避免算法陷入局部最優(yōu),加入基于Pareto熵的精英學(xué)習(xí)策略作為局部擾動(dòng)策略,精英學(xué)習(xí)率步長(zhǎng)可根據(jù)Pareto差熵和進(jìn)化狀態(tài)動(dòng)態(tài)調(diào)節(jié),從而形成一個(gè)閉環(huán)調(diào)節(jié)的進(jìn)化過(guò)程.最后,對(duì)多目標(biāo)柔性作業(yè)車間調(diào)度的經(jīng)典算例進(jìn)行求解,并與相關(guān)算法對(duì)比,仿真實(shí)驗(yàn)證明所提算法在收斂精度和機(jī)器分配均勻度方面具有明顯優(yōu)勢(shì).
[Abstract]:Aiming at the multi-objective flexible job shop scheduling problem, a chicken swarm algorithm based on Pareto entropy is proposed. Firstly, a multi-objective flexible job shop scheduling model is established. The optimal objectives are maximum completion time, maximum machine load and total machine load. Secondly, the concept of Pareto entropy is introduced into the chicken population algorithm. By calculating the entropy and difference entropy of the Pareto front end, the evolutionary state of the current population is judged, and the inertia weight is dynamically adjusted, which makes the adjustment process have pertinence and purpose. In order to avoid the algorithm falling into local optimum, the elite learning strategy based on Pareto entropy is added as the local disturbance strategy. The step size of elite learning rate can be dynamically adjusted according to Pareto difference entropy and evolutionary state, thus forming a closed-loop evolutionary process. Finally, the classical example of multi-objective flexible job shop scheduling is solved, and compared with the related algorithms, the simulation results show that the proposed algorithm has obvious advantages in convergence accuracy and machine distribution uniformity.
【作者單位】： 江南大學(xué)輕工過(guò)程先進(jìn)控制教育部重點(diǎn)實(shí)驗(yàn)室;
【基金】：國(guó)家自然科學(xué)基金項(xiàng)目(61572237,61573167)資助
【分類號(hào)】：TB497;TP18

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