Research on Robust Intelligent Planning Algorithm in Uncertainty Planning

摘要

Robust optimization is a better way to solve uncertainty planning the problem, and the ideas of combination optimization and quantum computation bring a new direction to solve the uncertainty planning problems by robust optimization. This paper firstly introduces simply the uncertainty problems, the researching situation of robust optimization and quantum computation and analyzes the necessity of Improved Quantum Particle Swarm Optimization (IQPSO) brought. Then. QPSO is improved from these aspects of the initial population, the solution space transformation, updating particle status, mutation etc.. and the process of IQPSO is given. Finally, the paper takes a robust multimodal function extreme optimization for example, to compare with other algorithms to analyze the convergence efficiency and operational performance of IQPSO. The simulation results show that IQPSO runs more stable, more efficient and faster convergence and can better solve the uncertainty planning problems.