Optimization Application of Ivy Algorithm in Distribution Network Restructuring

Driven by the “dual carbon” goal, the high penetration rate of distributed energy access and dynamic load fluctuations pose severe challenges to the reconstruction of distribution networks. This article introduces a new biomimetic optimization algorithm - Ivy Optimization Algorithm (Ivy), to solve the bottleneck problem of slow convergence speed and susceptibility to local optimal solutions in high-dimensional solution spaces of traditional optimization algorithms such as genetic algorithms and particle swarm optimization algorithms. This algorithm constructs a dual optimization mechanism that dynamically balances global exploration and local development by mathematically modeling the phototaxis orientation, collaborative competition mechanism, and adaptive growth strategy of the Ivy League population. It uses phototaxis simulation to achieve gradient tracking of the objective function, promotes co evolution of the solution set through neighborhood optimal guidance strategy, and avoids local extremum traps by combining velocity perturbation factors. Comparative experiments based on standard test functions and IEEE 33 node distribution network cases show that the IVY algorithm achieves an optimal value of 0.046 in the F1 function (an improvement of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$45.5 \%-72.6 \%$</tex> compared to traditional algorithms), reduces system losses by 18.3% in distribution network reconstruction, and improves convergence speed by over 40%. Its unique biome intelligent interaction model effectively solves the problem of rapid convergence of optimization solutions under complex constraints, providing an innovative and practical solution for topology optimization of intelligent distribution networks, and demonstrating significant advantages in improving power supply quality and optimizing operational efficiency.