基于多目标粒子群优化算法的输电网规划

Transmission network planning based on multi-objective particle swarm optimization algorithm

输电网规划是一个离散型、非线性、多目标的混合整数规划问题,难于求解.提出一种多目标粒子群优化算法用来求解输电网规划问题.在输电网规划模型中考虑了建设投资费用、运行费用及网损费用等3方面的因素.多目标粒子群优化算法基于Pareto支配关系来更新粒子的个体极值,并采用了精英归档技术,粒子的全局极值由档案库中的非劣解提供.使用Matlab7.1对Garver-6节点系统进行仿真计算,结果表明:与传统的单目标遗传算法相比,多目标粒子群优化算法获得的规划方案总费用更低,该方法可以提高输电网规划的经济性水平.

Transmission network planning is a discrete, nonlinear, multi-objective, and hybrid integer optimization problem difficult to be solved. A new multi-objective particle swarm optimization （MOPSO） algorithm was proposed to solve the problem. Three objectives, such as investment cost, run cost and transmission network wastage were considered in the planning model. The best particle position was updated based on Pareto dominance relationship in the MOPSO algorithm. Elitism archiving technique of non-dominated solutions was employed in the MOPSO algorithm and global particle best position was provided by non-inferior solutions in the archive. The experimental results in Garver-6 system on Matlab7.1 platform show that overall cost of the scheme obtained by MOPSO method is lower than that of the scheme obtained by traditional genetic algorithm. The MOPSO method can enhance economy level in transmission network planning problem.