Emergency material allocation and scheduling is a combination optimization problem, which is essentially a Non-deterministic Polynomial (NP) problem. Aiming at the problems such as slow convergence, easy prematurely falling into local optimum, and parameter constraints to solve high-dimensional and multi-modal combination optimization problems, this article proposes an adaptive weighted dynamic differential evolution (AWDDE) algorithm. The algorithm uses a chaotic mapping strategy to initialize the population. By weighting the standard differential evolution (DE) mutation strategy, a new weighted mutation operator is proposed. The scaling factor and cross probability can be adaptively adjusted. A disturbance operator is introduced to randomly generate the perturbation mutation and to accelerate the premature individuals to jump out of the local optimum. The algorithm is applied to the problem of emergency material allocation and scheduling, and a two-stage emergency material allocation and scheduling model is established. Compared with the standard DE algorithm and the chaos adaptive particle swarm algorithm, the results show that the AWDDE algorithm has the characteristics of stronger global optimization ability and faster convergence speed compared with other optimization algorithms, which provide assistance for smart cities research, including smart city services, applications, case studies, and policymaking considerations for emergency management.

中文翻译：

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应急物资调配调度的自适应加权动态差分进化算法

应急物资调配调度是一个组合优化问题，本质上是一个非确定性多项式（NP）问题。针对求解高维多模态组合优化问题的收敛慢、易过早陷入局部最优、参数约束等问题，提出一种自适应加权动态差分进化（AWDDE）算法。该算法使用混沌映射策略来初始化种群。通过对标准差分进化（DE）变异策略进行加权，提出了一种新的加权变异算子。可以自适应地调整比例因子和交叉概率。引入扰动算子随机产生扰动突变，加速早熟个体跳出局部最优。将该算法应用于应急物资调配调度问题，建立了两阶段应急物资调配调度模型。与标准DE算法和混沌自适应粒子群算法相比，结果表明AWDDE算法与其他优化算法相比具有更强的全局优化能力和更快的收敛速度等特点，为包括智慧城市在内的智慧城市研究提供了帮助。应急管理的服务、应用、案例研究和决策考虑。将该算法应用于应急物资调配调度问题，建立了两阶段应急物资调配调度模型。与标准DE算法和混沌自适应粒子群算法相比，结果表明AWDDE算法与其他优化算法相比具有更强的全局优化能力和更快的收敛速度等特点，为包括智慧城市在内的智慧城市研究提供了帮助。应急管理的服务、应用、案例研究和决策考虑。将该算法应用于应急物资调配调度问题，建立了两阶段应急物资调配调度模型。与标准DE算法和混沌自适应粒子群算法相比，结果表明AWDDE算法与其他优化算法相比具有更强的全局优化能力和更快的收敛速度等特点，为包括智慧城市在内的智慧城市研究提供了帮助。应急管理的服务、应用、案例研究和决策考虑。