Location and Capacity Determination Method of Electric Vehicle Charging Station Based on Simulated Annealing Immune Particle Swarm Optimization

As the number of electric vehicles(EVs)continues to grow and the demand for charging infrastructure is also increasing,how to improve the charging infrastructure has become a bottleneck restricting the development of EVs.In other words,reasonably planning the location and capacity of charging stations is important for development of the EV industry and the safe and stable operation of the power system.Considering the construction and maintenance of the charging station,the distribution network loss of the charging station,and the economic loss on the user side of the EV,this paper takes the node and capacity of charging station planning as control variables and the minimum cost of system comprehensive planning as objective function,and thus proposes a location and capacity planning model for the EV charging station.Based on the problems of low efficiency and insufficient global optimization ability of the current algorithm,the simulated annealing immune particle swarm optimization algorithm(SA-IPSO)is adopted in this paper.The simulated annealing algorithm is used in the global update of the particle swarm optimization(PSO),and the immune mechanism is introduced to participate in the iterative update of the particles,so as to improve the speed and efficiency of PSO.Voronoi diagram is used to divide service area of the charging station,and a joint solution process of Voronoi diagram and SA-IPSO is proposed.By example analysis,the results show that the optimal solution corresponding to the optimisation method proposed in this paper has a low overall cost,while the average charging waiting time is only 1.8 min and the charging pile utilisation rate is 75.5%.The simulation comparison verifies that the improved algorithm improves the operational efficiency by 18.1%and basically does not fall into local convergence.

考虑混合工艺的自动化码头多设备资源协同调度优化模型和算法设计

Considering the uncertainty of the speed of horizontal transportation equipment,a cooperative scheduling model of multiple equipment resources in the automated container terminal was constructed to minimize the completion time,thus improving the loading and unloading efficiencies of automated container terminals.The proposed model integrated the two loading and unloading processes of“double-trolley quay crane+AGV+ARMG”and“single-trolley quay crane+container truck+ARMG”and then designed the simulated annealing particle swarm algorithm to solve the model.By comparing the results of the particle swarm algorithm and genetic algorithm,the algorithm designed in this paper could effectively improve the global and local space search capability of finding the optimal solution.Furthermore,the results showed that the proposed method of collaborative scheduling of multiple equipment resources in automated terminals considering hybrid processes effectively improved the loading and unloading efficiencies of automated container terminals.The findings of this study provide a reference for the improvement of loading and unloading processes as well as coordinated scheduling in automated terminals.

Crowd Simulation and Its Applications: Recent Advances

This article surveys the state-of-the-art crowd simulation techniques and their selected applications, with its focus on our recent research advances in this rapidly growing research field. We first give a categorized overview on the mainstream methodologies of crowd simulation. Then, we describe our recent research advances on crowd evacuation,pedestrian crowds, crowd formation, traffic simulation, and swarm simulation. Finally, we offer our viewpoints on open crowd simulation research challenges and point out potential future directions in this field.

Optimization of remanufacturing process routes oriented toward eco-efficiency

Remanufacturing route optimization is crucial in remanufacturing production because it exerts a considerable impact on the eco-efficiency(i.e.,the best link between economic and environmental benefits)of remanufacturing.Therefore,an optimization model for remanufacturing process routes oriented toward eco-efficiency is proposed.In this model,fault tree analysis is used to extract the characteristic factors of used products.The ICAM definition method is utilized to design alternative remanufacturing process routes for the used products.Afterward,an eco-efficiency objective function model is established,and simulated annealing(SA)particle swarm optimization(PSO)is applied to select the manufacturing process route with the best eco-efficiency.The proposed model is then applied to the remanufacturing of a used helical cylindrical gear,and optimization of the remanufacturing process route is realized by MATLAB programming.The proposed model’s feasibility is verified by comparing the model’s performance with that of standard SA and PSO.

Application of a hybrid algorithm ePSOSA in well test parameter estimation

Estimating the significance parameters,such as skin factor,permeability,wellbore storage coefficient,are the most component of transient pressure analysis.Many optimization algorithms have been applied to parametric estimation and realized the minimum error of well test curve.Although a flexible heuristic particle swarm optimization can hunt optimal solution rapidly,it is difficult to search further in the vicinity of the optimal solution.Hence,to alleviate the local optimum and premature convergence,a global hybrid algorithm referred to as particle swarm simulated annealing is proposed,and proves to have better performance of convergence and accuracy than traditional methods,which are more suitable for parameter estimation.