Egret Swarm Optimized Distributed Power Flow Controller for Power Quality Enhancement in Grid Connected Hybrid System

P. M., Ansho; M., Germin Nisha

doi:10.1007/s42835-023-01679-6

J. Electr. Eng. Technol.

2023

DOI: 10.1007/s42835-023-01679-6

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Egret Swarm Optimized Distributed Power Flow Controller for Power Quality Enhancement in Grid Connected Hybrid System

Ansho P. M.,

Germin Nisha M.

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2024

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Cited by 3 publications

References 63 publications

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Developing and Analysing a Photovoltaic (PV) Renewable Energy Source with Particle Swarm Optimization (PSO) Algorithm for Battery Management in Grid Environment Power Quality

Arulmurugan,

Rajeswari,

Bharathidasan

et al. 2024

SN COMPUT. SCI.

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Developing and Analysing a Photovoltaic (PV) Renewable Energy Source with Particle Swarm Optimization (PSO) Algorithm for Battery Management in Grid Environment Power Quality

Arulmurugan,

Rajeswari,

Bharathidasan

et al. 2024

SN COMPUT. SCI.

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Leveraging enhanced egret swarm optimization algorithm and artificial intelligence-driven prompt strategies for portfolio selection

Huang,

Zhang,

Hua

et al. 2024

Sci Rep

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In the financial field, constructing efficient investment portfolios is a focal point of research, encompassing asset selection and optimization of asset allocation. With the advancements in Large Language Models (LLMs), generative Artificial Intelligence (AI) tools have showcased capabilities never seen before. However, the black-box nature of these tools renders their outputs difficult to interpret directly, often necessitating iterative fine-tuning to align with users’ expected outcomes. This study presents a structured prompt framework specifically designed for stock selection, aiming to provide direct and interpretable stock-selecting tools for investors of various levels. By creating representative scenarios and combining them into different cases for experimentation, we can explore how the construction of prompts influences the responses generated by generative AI tools. Additionally, this paper proposes a novel algorithm that combines the Nonlinear-Activated Beetle Antennae Search strategy with the Egret Swarm Optimization Algorithm (NBESOA) to address the Mean-Variance Portfolio Selection problem with Transaction Costs and Cardinality Constraints (MVPS-TCCC), utilizing real stock market data to construct portfolios based on generative AI tools recommendations. Simulation results indicate that, compared to other algorithms, NBESOA prefers optimizing portfolio configurations to achieve the highest Sharpe Ratio with the strictest constraints, bringing the outcomes closer to the portfolio’s efficient frontier.

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Vehicular Fog Resource Allocation Approach for VANETs Based on Deep Adaptive Reinforcement Learning Combined With Heuristic Information

Cheng,

Vijayaraj,

Sree Pokkuluri

et al. 2024

IEEE Access

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Egret Swarm Optimized Distributed Power Flow Controller for Power Quality Enhancement in Grid Connected Hybrid System

Cited by 3 publications

References 63 publications

Developing and Analysing a Photovoltaic (PV) Renewable Energy Source with Particle Swarm Optimization (PSO) Algorithm for Battery Management in Grid Environment Power Quality

Developing and Analysing a Photovoltaic (PV) Renewable Energy Source with Particle Swarm Optimization (PSO) Algorithm for Battery Management in Grid Environment Power Quality

Leveraging enhanced egret swarm optimization algorithm and artificial intelligence-driven prompt strategies for portfolio selection

Vehicular Fog Resource Allocation Approach for VANETs Based on Deep Adaptive Reinforcement Learning Combined With Heuristic Information

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