Improved Multi-Objective Quantum Genetic Algorithm for Solving Environmental Economic Dispatch Optimization Problems in Power Systems

This paper presents an Improved Multi-Objective Quantum Genetic Algorithm (IMQGA) for solving the environmental economic dispatch (EED) problem in power systems. IMQGA improves the uniformity of solution distribution and enhances initial diversity through angular coding of thermal power units and microhabitat population initialization. It also incorporates an adaptive grid division mechanism, which dynamically adjusts grid width and calculates congestion distances based on preference weighting. The algorithm balances global and local search capabilities using non-dominated sorting and elite strategies, ensuring the preservation of high-quality solutions on the Pareto front. Simulation results on the IEEE 6-machine, 30-node test system show that IMQGA outperforms existing algorithms in convergence speed, solution diversity, and accuracy, demonstrating its effectiveness for power system optimization and dispatch.