With the advancement of unmanned aerial vehicle (UAV) technology, UAVs, such as multi-rotor drones, have found widespread application in wireless sensor networks. In scenarios where multiple UAVs collaborate to gather sensor data from the field, it is essential to establish a path planning model that incorporates an accurate energy consumption model for these UAVs. The power consumption of a multi-rotor drone varies depending on its flight state. When UAVs traverse various locations, it is not only the power required for steady-level flight that must be considered, but also the power necessary for acceleration, deceleration, climbing, and turning. This paper presents a path planning model for multiple UAVs, termed the Multi-UAV Path Planning Considering Multiple Energy Consumptions (MUAVPP-MEC). The solution derived adheres to the constraint that UAV flight energy consumption should not exceed the maximum stored energy, with the goal of minimizing the total flight time across all UAV paths. To tackle the MUAVPP-MEC, this study proposes an improved Bee Foraging Learning Particle Swarm Optimization algorithm (IBFLPSO), which integrates the bee-foraging algorithm into the particle swarm optimization framework. The IBFLPSO facilitates an efficient real-number encoding and greedy segmenting sequence decoding strategy, translating the solution space of the problem into the search space of the algorithm. To improve the optimization capabilities of the algorithm, IBFLPSO utilizes the energy-constrained 2-opt as a local search operator. In Experiment 1, the proposed model and algorithm are validated through three distinct case studies, demonstrating the stability and efficacy of the methods. It is clearly observed that as the number of collection points increases, both the total cruising time and energy consumption of the model rise significantly, thus confirming the accuracy of the model. In Experiment 2, when compared with four other algorithms, IBFLPSO outperforms them in both the optimal and average solutions. Specifically, the optimal solution of IBFLPSO is 54.64%, 49.45%, 25.78%, and 22.92% better than those of the traditional PSO algorithm, PSO-2OPT algorithm, GA, and BFLPSO, respectively.

In humanitarian aid scenarios, the model of cumulative capacitated vehicle routing problem can be used in vehicle scheduling, aiming at delivering materials to recipients as quickly as possible, thus minimizing their wait time. Traditional approaches focus on this metric, but practical implementations must also consider factors such as driver labor intensity and the capacity for on-site decision-making. To evaluate driver workload, the operation times of relief vehicles are typically used, and multi-objective modeling is employed to facilitate on-site decision-making. This paper introduces a multi-objective cumulative capacitated vehicle routing problem considering operation time (MO-CCVRP-OT). Our model is bi-objective, aiming to minimize both the cumulative wait time of disaster-affected areas and the extra expenditures incurred by the excess operation time of rescue vehicles. Based on the traditional grey wolf optimizer algorithm, this paper proposes a dynamic grey wolf optimizer algorithm with floating 2-opt (DGWO-F2OPT), which combines real number encoding with an equal-division random key and ROV rules for decoding; in addition, a dynamic non-dominated solution set update strategy is introduced. To solve MO-CCVRP-OT efficiently and increase the algorithm’s convergence speed, a multi-objective improved floating 2-opt (F2OPT) local search strategy is proposed. The utopia optimum solution of DGWO-F2OPT has an average value of two fitness values that is 6.22% lower than that of DGWO-2OPT. DGWO-F2OPT’s average fitness value in the algorithm comparison trials is 16.49% less than that of NS-2OPT. In the model comparison studies, MO-CCVRP-OT is 18.72% closer to the utopian point in Euclidean distance than CVRP-OT.

As the capacity of individual offshore wind turbines increases, prolonged downtime (due to maintenance or faults) will result in significant economic losses. This necessitates enhancing the efficiency of vessel operation and maintenance (O&M) to reduce O&M costs. Existing research mostly focuses on planning O&M schemes for individual vessels. However, there exists a research gap in the scientific scheduling for state-of-the-art O&M vessels. To bridge this gap, this paper considers the use of an advanced O&M vessel in the O&M process, taking into account the downtime costs associated with wind turbine maintenance and repair incidents. A mathematical model is constructed with the objective of minimizing overall O&M expenditure. Building upon this formulation, this paper introduces a novel restructuring particle swarm optimization which is tailed with a bespoke encoding and decoding strategy, designed to yield an optimized solution that aligns with the intricate demands of the problem at hand. The simulation results indicate that the proposed method can achieve significant savings of 28.85% in O&M costs. The outcomes demonstrate the algorithm’s proficiency in tackling the model efficiently and effectively.

In this paper we consider two important objects of transportation, cost and customer satisfaction. The latter mainly depends on vehicle arrival time and expecting time of the customer. Whereas in the reality, road conditions varies at different time periods and affect the vehicle travelling speed. Meanwhile, transport cost, including fuel consumption, relate to load of vehicle. Correspondingly, mathematical model of vehicle routing problem with soft time windows and road factor (VRPSTWRF) was established in which transport cost, fuel consumption and customer satisfaction are considered. Multi-agent ant colony optimization is proposed in which the features of agent perceiving and reacting to the environment are applied reasonably. Adaptive information heuristic factor and pheromone expectation heuristic factor changing mechanism is used to improve global convergence ability. Pheromone is updated adaptively, the fuel consumption rate also considered, to ensure the convergence speed. 3-opt strategy was introduced to improve local search ability. Thus, multi-agent ant colony optimization (MACO) was constructed and used to solve 40-customer VRPSTWRF model. Experiments show that MACO proposed is feasible and valid.

Based on the air-ground collaborative logistics distribution model using UAVs and vehicles, this paper addresses the logistics scenario of delivering before picking up. Considering factors such as customer priority, vehicle cost, and UAV cost, with the objective of minimizing the total travel cost, we propose the Air-ground Collaborative Logistics Transportation Scheduling Problem with Pickup and Delivery Considering Customer Priority (ALTSPPDCP). Based on the characteristics of the model, the use of heuristic algorithms can efficiently solve such optimization problems, avoiding the time-consuming exhaustive search and improving the quality of the solutions. This paper designs a multi-layer, multi-stage encoding and decoding strategy based on the Three-Learning Strategy Particle Swarm Optimization algorithm, integrating the ascending order sorting method and dynamic segmentation method to transform the particle space into the model space. An intelligence optimization algorithm for solving ALTSPPDCP is proposed. Finally, In the 50-node scenario of the model comparison, the vehicle-UAV schema achieved a total cost that was 14.86% lower than that of the vehicle-only schema. In the algorithm comparison experiment, the optimal solution obtained by TSLPSO reduced costs by 39.99% and 27.94% compared to PSO and RPSO, respectively.