Adaptive infinite impulse response system identification using an enhanced golden jackal optimization

Kong

et al. 2023

J. Opt.

In recent years, the azimuthal mode, also known as orbital angular momentum (OAM), and the radial mode of Laguerre Gaussian (LG) beam have been applied in many disparate areas of physics. Identification of them is the basic technique for these applications. Although there are many recognition methods, each has its own problems. Either it can only be used to distinguish one of OAM and radial modes, or the number of modes that can be distinguished is too small, or the operation is too complex. So, a great challenge is to design a method that is not only simple, but also applicable to the identification of both OAM and radial modes with a high dimension. Here, by introducing the strong mode selectivity, we propose a convenient method for identification of OAM and radial modes based on multiplexing holography. In our method, only a single hologram is need. Experiment on the identification of 100 OAM modes (l = –50, –49, …, +49, +50) has been carried out and demonstrates the feasibility of our design. What’s more, our method is also applicable to the identification of radial modes, and then to the identification of LG modes. In experiment, we have realized the identification of 8 radial modes (p = 1, 2, 3, …, 8) and 120 LG modes (p = 1, 3, 5, and l = –20, –19, ..., +20). Our method should have important applications in high-capacity optical communication based on OAM modes, radial modes, and/or LG modes.

Section: Design Principle and Experimental Results Of High-dimensiona...mentioning

confidence: 99%

Identification of both orbital angular momentum and radial mode based on multiplexing holography

Kong

et al. 2023

J. Opt.

“…Golden jackals frequently forage in the form of golden jackal pairs, with male jackals doing the hunting while the female jackals follow, and they usually forage in groups. Multiple golden jackals will collaborate to hunt, increasing the success rate [36]. When foraging, golden jackals will adjust their search strategies based on the abundance of food and the location of other competitors to acquire more food resources [37].…”

Section: Golden Jackal Optimization Algorithm (Gjo)mentioning

confidence: 99%

A Multi-Objective Optimization Problem Solving Method Based on Improved Golden Jackal Optimization Algorithm and Its Application

Jiang,

Yue,

Chen

et al. 2024

Biomimetics

The traditional golden jackal optimization algorithm (GJO) has slow convergence speed, insufficient accuracy, and weakened optimization ability in the process of finding the optimal solution. At the same time, it is easy to fall into local extremes and other limitations. In this paper, a novel golden jackal optimization algorithm (SCMGJO) combining sine–cosine and Cauchy mutation is proposed. On one hand, tent mapping reverse learning is introduced in population initialization, and sine and cosine strategies are introduced in the update of prey positions, which enhances the global exploration ability of the algorithm. On the other hand, the introduction of Cauchy mutation for perturbation and update of the optimal solution effectively improves the algorithm’s ability to obtain the optimal solution. Through the optimization experiment of 23 benchmark test functions, the results show that the SCMGJO algorithm performs well in convergence speed and accuracy. In addition, the stretching/compression spring design problem, three-bar truss design problem, and unmanned aerial vehicle path planning problem are introduced for verification. The experimental results prove that the SCMGJO algorithm has superior performance compared with other intelligent optimization algorithms and verify its application ability in engineering applications.

“…[18,19] Depending on the traditional statistics of region-wise vehicle density under night and day, the entire area is separated into three zones. [20,21] Zone-wise allocation distributes the CS to serve the greatest user count in urban regions equally. The goal of choosing the best FCS locations, the best PV locations with the best sizes, and the best battery scheduling at the distribution network is to lessen energy loss.…”

Section: Structure Of Locating the Fcs And Pv With Bes In Distributio...mentioning

confidence: 99%

A Hybrid Technique for Optimal Placement of Fast‐Charging Stations of Electric Vehicles for the Reliability of Distribution Network

Ranjith Kumar,

Vallimurugan

2023

Energy Tech

Proper planning for electric vehicle (EV) charging stations, as well as the necessary network development, is critical for the continued growth of EVs and conventional loads. This article proposes a hybrid method for the allocation of fast‐charging stations (FCSs) and photovoltaic (PV) with battery energy storage (BES) and scheduling. The proposed hybrid technique is the wrapper of golden jackal optimization (GJO) and random forest algorithms (RFA), commonly named the GJO–RFA technique. Using the GJO approach, the allocation and EV assignment problems are resolved. RFA is used to address EV, traffic flow, and PV‐related uncertainties. The objective of the proposed approach is to minimize the loss of energy, investments, index of voltage deviation, and operation and maintenance costs of FCS, PV, and BES. The associated important factors are also assessed, including the number of charging ports, FCS capacity, and EV flow caught using a FCS. As a test example, a 33‐node radial distribution network with the appropriate traffic network is used. For the final problem, the proposed approach to minimal energy loss is 2622.7270 kWh. By then, the performance of the proposed approach is executed in MATLAB, and it is contrasted with other existing techniques. The result shows that the proposed method‐related energy loss is less than existing approaches.