Multi-objective evolutionary algorithm based on decision space partition and its application in hybrid power system optimisation

Yang, Guanci; Zhang, Ansi; Li, Shaobo; Wang, Yang; Wang, Yunan; Quan, Xie; He, Ling

doi:10.1007/s10489-016-0864-1

Cited by 13 publications

(6 citation statements)

References 20 publications

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“…Its advantage is that it does not need the weight coefficient of each target in the man-made model. It also has a faster convergence rate and robustness [37][38][39]. Based on the NSGA-II algorithm, we can solve the multi-objective model in the state and decision variables.…”

Section: Nsga-ii-based Algorithm To Obtain the Nash Equilibrium Pointmentioning

confidence: 99%

Competitive–Cooperative Strategy Based on Altruistic Behavior for Dual-Channel Supply Chains

Wang

2018

Sustainability

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Abstract:As market competition becomes increasingly fierce, it becomes more and more important for members of the supply chain to maximize market sales and improve the economic benefits of all parties through altruistic cooperation. Considering the complex relationship between online and offline retail channels, this paper proposes a competitive-cooperative strategy based on altruistic behavior for the dual-channel supply chain, by applying the theory of the co-competition game. First, we introduce the problem with respect to the relationship between online and offline retail channels, and establish the competitive-cooperative strategy model based on altruistic behavior. Then, we prove the equilibrium strategy for existence and stability of the proposed model through mathematical deduction. Next, a multi-object optimal model is excluded by applying the Pareto principle, and the NSGA-II-based algorithm is obtained to acquire the Nash equilibrium point. Finally, we present the case testing results, which indicate that the proposed model is robust and can improve the channel efficiency of the supply chain.

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Section: Nsga-ii-based Algorithm To Obtain the Nash Equilibrium Pointmentioning

confidence: 99%

Competitive–Cooperative Strategy Based on Altruistic Behavior for Dual-Channel Supply Chains

Wang

2018

Sustainability

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“…Control strategies based on global optimization generally require an optimized action search under conditions where the global disturbances are known, such as in dynamic programming (DP), genetic algorithms, and game theory methods [90][91][92]. However, due to its computationally intensive and complex preview, this class of algorithms is not usually supported for real-time applications.…”

Section: Optimization-based Energy Management Strategiesmentioning

confidence: 99%

An Overview of Modelling and Energy Management Strategies for Hybrid Electric Vehicles

2023

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With the world’s energy reserves under strain and the requirements of national carbon emission regulations, the fuel efficiency and environmental friendliness of automobiles are becoming increasingly important. Due to the combination of long cruising range and energy efficiency, hybrid electric vehicles (HEVs) have been adopted as a reliable option for improving fuel economy and reducing emissions. In order to fully exploit the advantages of hybrid electric vehicles, energy management and torque distribution have become the focus of control strategies for HEVs, while also ensuring battery life and meeting requirements for fuel consumption, emissions and driving performance. Therefore, a great deal of research has been carried out on energy management strategies and many approaches have been offered in the literature. This review provides a comprehensive assessment of the literature, highlighting its contributions and making a complete reference for scholars interested in hybrid vehicle development, control, and optimization.

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“…(5) Judge whether the iteration meets the termination condition of particle swarm optimization [18].…”

Section: Energy Management Strategymentioning

confidence: 99%

Real-Time Application Optimization Control Algorithm for Energy Management Strategy of the Hybrid Power System Based on Artificial Intelligence

Liu

2021

Mobile Information Systems

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In recent years, due to the strengthening of our country’s comprehensive strength, the rapid development of science and technology and artificial intelligence has also attracted people’s attention. Artificial intelligence is a highly applicable subject, which has very good applications in power systems. In the experiment, the open circuit voltage method and the ampere-hour integration method are used to estimate the SOC of the lithium battery and the particle swarm energy management algorithm is used to allocate the output power of the fuel cell and the lithium battery. The particle swarm algorithm module calls the dual source hybrid power system module through the sim function to convert the actual value input in the system into a fuzzy quantity suitable for fuzzy control. The energy management strategy based on particle swarm optimization and fuzzy control was tested based on working conditions under the comprehensive test bench. Finally, the matching of the hybrid system is analyzed from the structure, component parameters, control strategy, and driving cycle of the vehicle. The experimental data show that the total fuel consumption of the three sets of experiments is averaged to get a fuel consumption rate of 26.3 m3/100 km for the hybrid city bus under the optimized energy management strategy. The results show that the real-time energy management strategy based on particle swarm algorithm can significantly improve the real-time performance of traditional instantaneous energy management strategies while reducing fuel consumption.

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Multi-objective evolutionary algorithm based on decision space partition and its application in hybrid power system optimisation

Cited by 13 publications

References 20 publications

Competitive–Cooperative Strategy Based on Altruistic Behavior for Dual-Channel Supply Chains

Competitive–Cooperative Strategy Based on Altruistic Behavior for Dual-Channel Supply Chains

An Overview of Modelling and Energy Management Strategies for Hybrid Electric Vehicles

Real-Time Application Optimization Control Algorithm for Energy Management Strategy of the Hybrid Power System Based on Artificial Intelligence

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