Cross-border e-commerce logistics activities increasingly use multimodal transportation modes. In this transportation mode, the use of high-performance optimizers to provide decision support for multimodal transportation for cross-border e-commerce needs to be given attention. This study constructs a logistics distribution optimization model for cross-border e-commerce multimodal transportation. The mathematical model aims to minimize distribution costs, minimize carbon emissions during the distribution process, and maximize customer satisfaction as objective functions. It also considers constraints from multiple dimensions, such as cargo aircraft and vehicle load limitations. Meanwhile, corresponding improvement strategies were designed based on the Sand Cat Swarm Optimization (SCSO) algorithm. An improved swarm intelligence algorithm was proposed to develop an optimizer based on the improved swarm intelligence algorithm for model solving. The effectiveness of the proposed mathematical model and improved swarm intelligence algorithm was verified through a real-world case of cross-border e-commerce logistics transportation. The results indicate that using the proposed solution in this study, the cost of delivery and carbon emissions can be reduced, while customer satisfaction can be improved.

In response to the practice of rescue channel blocking and a shortage of emergency materials in the event of sudden significant disasters, a multi-UAV collaborative distribution scheme was designed based on the demand for rapid and accurate distribution of materials. This paper constructed a multi-UAV collaborative task assignment and routing problem with simultaneous delivery and pick-up and time windows (MVTARPSDPTW), considering the factors of UAV load, energy consumption, cargo quality, and volume to minimize the total cost of UAV distribution and the full penalty of the task, as well as optimizing the balance of UAV efficiency. This paper proposes a multi-objective, improved brainstorming optimization algorithm based on Pareto dominance (MIBSO) to solve the MVTARPSDPTW problem. With DTLZ4, DTLZ5, and DTLZ6 benchmarks, this work tests the algorithm performance according to the characteristic attributes of the model sought, selecting the four indicators of GD, the Spacing metric, HV, and IGD, concerning convergence, solution distribution, and comprehensive performance. Case validation is based on a COVID-19 scenario in Changchun, China, and the results show that the model algorithm designed in this paper has good performance and feasibility in convergence and distribution of reconciliation. Finally, the multi-UAV emergency material distribution solution provides practical, theoretical support for rescue tasks in sudden significant disasters.

Near space airships have gained widespread attention owing to the unique characteristics of long endurance at high altitudes; because of this characteristic, such airships have great potential for use in the fields of communication relay, early warning, earth observation etc. Near space airships can substitute near-earth geostationary satellites owing to the advantages of high load and long endurance at altitude exceeding 20 km. Buoyancy against gravity is generated by the airship hull, which is completely filled with helium, while the near space airship is equipped with an electrical propulsion system powered by solar energy. Unlike conventional aircraft, nearspace airships are a new type of vehicles with less design experience; coupled multi-disciplinary problems related to aerodynamics, structure, energy, control, etc. exist. With the aim of finding a solution for optimal near-space airship design, this study proposed a design using a multidisciplinary design optimization method.

Automated guided vehicles (AGVs) are one of the core technologies for building unmanned autonomous integrated automated electric meter verification workshops in metrology centers. However, complex obstacles on the verification lines, frequent AGV charging, and multi-AGV collaboration make the scheduling problem more complicated. Aiming at the characteristics and constraints of AGV transportation scheduling for metrology verification, a multi-AGV scheduling model was established to minimize the maximum completion time and charging cost, integrating collision-avoidance constraints. An improved snake optimization algorithm was proposed that first assigns and sorts tasks based on AGV-order-address three-level mapping encoding and decoding, then searches optimal paths using an improved A* algorithm solves multi-AGV path conflicts, and finally finds the minimum-charging-cost schedule through large neighborhood search. We conducted simulations using real data, and the calculated results reduced the objective function value by 16.4% compared to the traditional first-in-first-out (FIFO) method. It also reduced the number of charges by 60.3%. In addition, the proposed algorithm is compared with a variety of cutting-edge algorithms and the results show that the objective function value is reduced by 8.7–11.2%, which verifies the superiority of the proposed algorithm and the feasibility of the model.

With the gradual promotion and construction of zero-carbon parks on the plateau, the integration of highly random renewable energy power sources has increased the complexity of multi-energy economic dispatch within the parks. The efficient optimization of economic operation in zero-carbon park multi-energy systems (ZCPMESs) including wind power and photovoltaic power is facing significant challenges. In view of problems such as slow convergence speed, low solution accuracy, and poor algorithm robustness of the existing optimization methods in dealing with the economic operation optimization problem of ZCPMES, this paper proposes a hybrid multi-strategy improved beluga whale optimization (HMIBWO) algorithm specifically designed to address these challenges. By designing an inter-species information sharing mechanism and virtual-guided search strategy, the convergence rate of the algorithm is enhanced while effectively balancing its global search and local development capabilities. Further, adaptive mutation strategy is introduced to avoid the problem of search stagnation in HMIBWO and improve the search efficiency of the algorithm. To evaluate the effectiveness of HMIBWO in the economic operation optimization problem of ZCPMES, the solution capability was first verified on 18 benchmark problems, following which it was applied to two typical scenarios and compared against state-of-the-art heuristic algorithms: conventional and highly random. The experimental results show that the power gap of the HMIBWO optimized scheme can be reduced to 3.79%, and the wind and solar power consumption rates can reach 76.99% and 72.68%, indicating that HMIBWO can be effectively applied to optimize the operation of ZCPMES.