Improved NSGA-II to solve a novel multi-objective task allocation problem with collaborative tasks

Xu, Weiye; Pi, Dawei; Wang, Hongliang; Xie, Boyuan

doi:10.1177/09544070211072665

Cited by 3 publications

(2 citation statements)

References 23 publications

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“…However, as the problem scale increases, the difficulty of solving this type of algorithm increases, and heuristic algorithms become the best choice. The previous research revealed that NSGA-II has the advantages of simple structure and high searchability, and it is widely used to solve the VRP (41). With regard to algorithm selection, NSGA-II was selected.…”

Section: Solution Methodsmentioning

confidence: 99%

Integrated Emergency Vehicle Scheduling Model in Urban Emergency Rescue within a Time-Limited Period

Li,

Wang,

et al. 2023

Transportation Research Record: Journal of the Transportation R

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Considering the uncertainty of urban emergency rescue time, it is difficult for emergency vehicles to maintain a 100% service level continuously. The biggest challenge is to minimize driving distance and costs when the number of vehicles available for deployment is uncertain. In this study, considering the limitation of emergency vehicles, a new integrated emergency vehicle scheduling model for large-scale urban emergency rescue was developed, which is able to tackle multiple emergency tasks at the same time, achieving the shortest path and the lowest cost. Then, the emergency scheduling was simulated in seven urban areas of H City in China to verify the applicability of the proposed model, during which a fast non-dominated sorting multi-objective genetic algorithm with elite strategy was used to solve this model. Moreover, the corresponding algorithm test was conducted to verify the feasibility of the Pareto solution and demonstrate its effectiveness. Moreover, the simulation results show that the used algorithm can reduce computing time by 14.27%, compared with the genetic algorithm. Finally, the optimal scheduling scheme was selected according to the decision-makers’ preferences. This study solved various emergency vehicle scheduling problems under uncertain emergency times, which was beneficial for multilateral cooperation emergency scheduling and provided decision support for emergency command departments for choosing the best emergency scheme.

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Section: Solution Methodsmentioning

confidence: 99%

Integrated Emergency Vehicle Scheduling Model in Urban Emergency Rescue within a Time-Limited Period

Li,

Wang,

et al. 2023

Transportation Research Record: Journal of the Transportation R

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“…The benchmark function was the test tool and the suspension system was the simulation environment. The results showed that the algorithm had good applicability and potential in parameter optimization [17].…”

Section: Related Workmentioning

confidence: 95%

Energy Consumption Optimization Strategy of Hybrid Vehicle Based on NSGA-II Genetic Algorithm

Wang

Gao

2023

Processes

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Hybrid electric vehicles (HEVs) have certain advantages over internal combustion engines in terms of energy consumption and emission performance. However, the transmission system parameters are uncertain. The low matching between the engine and the power transmission system makes it a big problem to improve the efficiency of hybrid vehicles. Therefore, the multi-objective optimization design of hybrid vehicles is studied. The transmission system parameters of hybrid vehicles are analyzed from the objective function, decision variables, and constraints. The NSGA-II algorithm with elite strategy is introduced to realize the optimal selection of parameters and formulation of energy consumption optimization strategy. The results showed that the multi-objective optimization algorithm could adjust the position of the working point of the engine and improve the efficiency by more than 10%. There was an average difference of 2.15% after the improvement in the fuel consumption of four-gear vehicles. The fuel consumption per 100 km decreases by more than 3%. The maximum climbing gradient of the whole vehicle was 33.9%. The power factor of the direct gear of the maximum power factor increases by 15% after the improvement. The multi-objective energy consumption optimization design of hybrid vehicles proposed in the study can effectively improve the economic and dynamic performance of the whole vehicle and reduce fuel consumption. It provides a reference for the optimization of the hybrid vehicle transmission system.

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Multi-UAV task allocation based on GCN-inspired binary stochastic L-BFGS

Zhang,

et al. 2023

Computer Communications

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Improved NSGA-II to solve a novel multi-objective task allocation problem with collaborative tasks

Cited by 3 publications

References 23 publications

Integrated Emergency Vehicle Scheduling Model in Urban Emergency Rescue within a Time-Limited Period

Integrated Emergency Vehicle Scheduling Model in Urban Emergency Rescue within a Time-Limited Period

Energy Consumption Optimization Strategy of Hybrid Vehicle Based on NSGA-II Genetic Algorithm

Multi-UAV task allocation based on GCN-inspired binary stochastic L-BFGS

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