Research on microgrid scheduling based on Cauchy Variation improved Crow search algorithm

The Crow Search Algorithm (CSA) is known for its straightforward implementation and robust optimization capability, making it a popular choice for solving complex nonlinear problems. However, CSA has a notable limitation: its reliance on individual experience for searching candidate solutions, which becomes particularly pronounced when tackling high-dimensional functions. To address these shortcomings, this paper proposes an enhanced crow search algorithm (CSA-PSO) inspired by the principles of Particle Swarm Optimization (PSO). The improved algorithm facilitates information sharing at both the individual and population levels, enabling crows to adopt a collaborative search strategy. Additionally, a Cauchy mutation mechanism is incorporated to prevent premature convergence. By applying Cauchy perturbation to the top $20 \%$ of individuals ranked by fitness value, the diversity of the population is maintained, ensuring a balanced approach between exploitation and exploration. To evaluate the effectiveness of CSA-PSO, the algorithm was tested on 17 benchmark functions and compared with other intelligent optimization techniques. Results demonstrate that CSA-PSO achieves superior performance in terms of search accuracy and convergence speed. Furthermore, the algorithm was successfully applied to energy scheduling in microgrids. By solving the objective function, the optimal power output scheme for distributed energy sources was determined, improving the economic efficiency of microgrid operations and validating the algorithm applicability.