Power grid loss optimization based on artificial bee colony algorithm

In this paper, a multi-strategy improved artificial bee colony algorithm (MSIABC) is proposed based on the limitations of the traditional artificial bee colony algorithm in power grid loss optimization, such as insufficient convergence accuracy and easy to fall into local optimal. The algorithm enhances the orientation of population evolution through a differentiated search mechanism guided by dual-elite individuals, introduces an immune-adaptive state switch model to achieve a dynamic balance between development and exploration, and combines the variable scale neighborhood search strategy driven by Levy flight to effectively improve the global optimization ability of complex solution Spaces. In the benchmark function test, MSIABC shows significant advantages in both 30-dimensional and 60-dimensional scenarios: the quality of the solution in low-dimensional environment is tens of orders higher than that of the traditional algorithm, and it still maintains a ten-tens times lead in high-dimensional environment, which validates its dimensional robustness and strategy effectiveness. On this basis, a multi-constraint model with the objective of comprehensive operation cost minimization is constructed, which covers distributed power cooperative scheduling and pollutant treatment cost optimization. The results show that MSIABC is superior to PSO, ABC and their improved variant algorithms in terms of optimal solution quality, stability and convergence efficiency, and the comprehensive cost optimization range is more than 10% higher than the traditional method.