A multi-strategy firefly algorithm based on rough data reasoning for power economic dispatch

Ning Zhou; Chen Zhang; Songlin Zhang; Ning Zhou; Chen Zhang; Songlin Zhang

doi:10.3934/mbe.2022411

Mathematical Biosciences and Engineering

2022, Volume 19, Issue 9: 8866-8891. doi: 10.3934/mbe.2022411

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Research article Special Issues

A multi-strategy firefly algorithm based on rough data reasoning for power economic dispatch

School of Electronics and Information Engineering, Lanzhou Jiaotong University, Lanzhou 730070, China

Received: 19 February 2022 Revised: 30 May 2022 Accepted: 01 June 2022 Published: 17 June 2022

Dynamic economic dispatch (DED) is a multi constraint and nonlinear complex problem, which is embodied in the dynamic decision-making coupled with each other in time and space. It is generally transformed into a high-dimensional multi constraint optimization problem. In this paper, a multi Strategy firefly algorithm (MSRFA) is proposed to solve the DED problem. MSRFA puts forward three strategies through the idea of opposite learning strategy and rough data reasoning to optimize the initialization and iteration process of the algorithm, improve the convergence speed of the algorithm in medium and high dimensions, and improve the escape ability when the algorithm falls into local optimization; The performance of MSRFA is tested in the simulation experiment of DED problem. The experimental results show that MSRFA can search the optimal power generation cost and minimum load error in the experiment, which reflects MSRFA superior stability and ability to jump out of local optimization. Therefore, MSRFA is an efficient way to solve the DED problem.
- power system dynamic economic dispatch,
- firefly algorithm,
- opposing learning strategies,
- rough data reasoning,
- relational reasoning
Citation: Ning Zhou, Chen Zhang, Songlin Zhang. A multi-strategy firefly algorithm based on rough data reasoning for power economic dispatch[J]. Mathematical Biosciences and Engineering, 2022, 19(9): 8866-8891. doi: 10.3934/mbe.2022411

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Abstract

Dynamic economic dispatch (DED) is a multi constraint and nonlinear complex problem, which is embodied in the dynamic decision-making coupled with each other in time and space. It is generally transformed into a high-dimensional multi constraint optimization problem. In this paper, a multi Strategy firefly algorithm (MSRFA) is proposed to solve the DED problem. MSRFA puts forward three strategies through the idea of opposite learning strategy and rough data reasoning to optimize the initialization and iteration process of the algorithm, improve the convergence speed of the algorithm in medium and high dimensions, and improve the escape ability when the algorithm falls into local optimization; The performance of MSRFA is tested in the simulation experiment of DED problem. The experimental results show that MSRFA can search the optimal power generation cost and minimum load error in the experiment, which reflects MSRFA superior stability and ability to jump out of local optimization. Therefore, MSRFA is an efficient way to solve the DED problem.

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