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

Area of Science:

• Optimization algorithms
• Electrical power systems engineering

Background:

• Dynamic economic dispatch (DED) is a complex, multi-constraint, nonlinear optimization problem.
• DED involves coupled dynamic decision-making across time and space, often resulting in high-dimensional problems.

Purpose of the Study:

• To propose a novel Multi-Strategy Firefly Algorithm (MSRFA) for solving the Dynamic Economic Dispatch (DED) problem.
• To enhance the initialization and iteration processes of firefly algorithms for improved performance in medium to high dimensions.

Main Methods:

• The proposed MSRFA incorporates opposite learning strategy and rough data reasoning.
• These strategies aim to optimize the algorithm's initialization and iteration, enhancing convergence speed.
• The algorithm's ability to escape local optima is also improved.

Main Results:

• MSRFA demonstrated superior stability in simulation experiments for the DED problem.
• The algorithm successfully minimized power generation cost and load error.
• MSRFA showed a significant ability to escape local optimization traps.

Conclusions:

• The Multi-Strategy Firefly Algorithm (MSRFA) is an efficient and effective method for solving the Dynamic Economic Dispatch (DED) problem.
• MSRFA offers improved stability and a better ability to find global optima compared to standard algorithms.