Optimization of jamming formation of USV offboard active decoy clusters based on an improved PSO algorithm

Wu, Zhaodong; Luo, Yasong; Hu, Shengliang

doi:10.1016/j.dt.2023.03.017

Cited by 3 publications

(3 citation statements)

References 18 publications

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“…The power curves of the shiftable load and the total load in a scheduling period are shown in Figure 5. The PSO algorithm is a mature and robust optimization algorithm that has been verified over many years [35][36][37]. PSO is widely used to solve the optimization problem due to its simplicity and practicality.…”

Section: Demand Responsementioning

confidence: 99%

“…Hence, PSO is selected to solve the demand response model in a simple scenario in the paper. Taking the lowest power purchase cost as the The PSO algorithm is a mature and robust optimization algorithm that has been verified over many years [35][36][37]. PSO is widely used to solve the optimization problem due to its simplicity and practicality.…”

Section: Demand Responsementioning

confidence: 99%

“…To verify the superiority of the improved WSO algorithm, different algorithms and the WSO algorithm were selected to calculate the proposed model in scenario 3 [37][38][39]. The parameter settings of each algorithm are shown in Table 7.…”

Section: Algorithm Comparisonmentioning

confidence: 99%

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Economic Optimization Scheduling Based on Load Demand in Microgrids Considering Source Network Load Storage

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et al. 2023

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As a large number of flexible elements such as distributed power and flexible load are connected to microgrids, the economic improvement of microgrids has become an important topic through rational energy distribution. In this paper, an economic scheduling model considering the load demand for a microgrid system under the mechanism of a peak–valley tariff is proposed. A mathematical model of the microgrid components is proposed to determine the exchange power between the microgrid and main network. Meanwhile, an improved War Strategy Optimization (WSO) algorithm is proposed to investigate three scenarios: (i) without batteries, (ii) with batteries and (iii) with batteries and demand response. Additionally, demand response optimization is carried out with the Particle Swarm Optimization (PSO) algorithm and the improved WSO is compared with four other algorithms in scenario (iii). The comparison shows that the improved WSO algorithm has a better optimization performance in solving the proposed scheduling model.

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Section: Demand Responsementioning

confidence: 99%

Section: Demand Responsementioning

confidence: 99%

See 1 more Smart Citation

Economic Optimization Scheduling Based on Load Demand in Microgrids Considering Source Network Load Storage

Sun

et al. 2023

Electronics

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Jamming effect analysis and reticle parameter identification of infrared spin-scan seeker under dynamic engagement environment

Li,

Chen,

Chen

et al. 2024

Infrared Physics & Technology

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Distributed Communication Interference Resource Scheduling using the Master-Slave Parallel Scheduling Genetic Algorithm

Wei,

Wu,

Zhan

et al. 2024

Preprint

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With the increasing intelligence and diversification of communication interference in recent years, communication interference resource scheduling has received more attention. However, the existing interference scenario models have been developed mostly for remote high-power interference with a fixed number of jamming devices without considering power constraints. In addition, there have been fewer scenario models for short-range distributed communication interference with a variable number of jamming devices and power constraints. To address these shortcomings, this study designs a distributed communication interference resource scheduling model based distributed communication interference deployment and system operational hours and introduces the stepped logarithmic jamming-to-signal ratio. The proposed model can improve the scheduling ability of the master-slave parallel scheduling genetic algorithm (MSPSGA) in terms of the number of interference devices and the system’s operational time by using four scheduling strategies referring to the searching number, global number, master-slave population power, and fixed-position power. The experimental results show that the MSPSGA can improve the success rate of searching for the minimum number of jamming devices by 40% and prolong the system’s operational time by 128%. In addition, it can reduce the algorithm running time in the scenario with a high-speed countermeasure, the generation time of the jamming scheme, and the average power consumption by 4%, 84%, and 57%, respectively. Further, the proposed resource scheduling model can reduce the search ranges for the number of jamming devices and the system’s operational time by 93% and 79%, respectively.

show abstract

Optimization of jamming formation of USV offboard active decoy clusters based on an improved PSO algorithm

Cited by 3 publications

References 18 publications

Economic Optimization Scheduling Based on Load Demand in Microgrids Considering Source Network Load Storage

Economic Optimization Scheduling Based on Load Demand in Microgrids Considering Source Network Load Storage

Jamming effect analysis and reticle parameter identification of infrared spin-scan seeker under dynamic engagement environment

Distributed Communication Interference Resource Scheduling using the Master-Slave Parallel Scheduling Genetic Algorithm

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