Improved Binary Particle Swarm Optimization Algorithm for Distribution Network Reconfiguration

In the power industry, distribution network reconfiguration represents a critical optimization challenge. Various algorithms have been developed to address this problem, with the improved binary particle swarm optimization (BPSO) algorithm proving particularly effective. This algorithm employs binary encoding to represent switch status (0 for open, 1 for closed) and incorporates enhanced velocity-position conversion using sigmoid transformation functions. The improved BPSO implements dynamic inertia weight adjustment and optimized learning factors to balance exploration and exploitation capabilities. The algorithm's fitness function typically evaluates multiple objectives including power loss minimization, voltage profile improvement, and load balancing through weighted sum approaches. Implementation involves constraint handling for radial topology maintenance using graph theory checks and penalty methods for violating operational constraints. Widely applied in distribution network reconfiguration scenarios, this enhanced algorithm demonstrates significant improvements in computational efficiency and solution quality. It effectively enhances distribution system reliability and economic performance while reducing energy consumption and environmental impact. Consequently, the improved BPSO algorithm shows substantial application potential across power industry applications. Ongoing research continues to refine this algorithm through hybridization with other optimization techniques, parallel computing implementations, and multi-objective optimization frameworks to further expand its performance capabilities and application scope. Recent innovations include incorporating machine learning for parameter tuning and integrating real-time operational data for adaptive reconfiguration strategies.