Based On Differential Evolution Algorithm And Quantum Evolutionary Algorithm Of Three Optimization Scheduling Problem

Scheduling optimization problem is a very important practical problem in the real production and living, How to design efficient intelligent algorithm to solve scheduling optimization problem has been attracting the attention of the academic and the engineering. Scheduling optimization is the research object of this thesis. Several algorithms are designed to deal with flow-shop scheduling problems and vehicle routing problems. The main contents of this thesis are as follows:(1) Differential evolution algorithm and Quantum evolution algorithm are both used to review flow-shop scheduling problem and vehicle routing problem.(2) A differential evolution algorithm with two speed-up methods (DE_TSM) is presented for solving the no-wait flow-shop scheduling problem (NFSSP) with sequence-dependent setup times (SDSTs) and release dates (RDs). The criterion is to minimize the total completion time. Simulation results demonstrate the superiority of DE TSM in terms of searching quality and efficiency.(3) A hybrid quantum-differential evolution algorithm (HQEA) based on qubits population with the angle encoding, adaptive and reduced update tactic, and problem dependent neighborhood search is proposed for dealing with the capacitated vehicle routing problem. The simulation tests proved the superiority of HQEA.(4) A novel assigns method with the boundary custom distribution based on nearest assigns method is designed to convert the multi-depot vehicle routing problem (MDVRP) to numbers of single-depot vehicle routing problems. Then, a hybrid algorithm proposed in (3) is used to solve each single-depot vehicle routing problem. Simulation results show the effectiveness of the improved assign method and the hybrid algorithm.