改进型遗传算法在煤矿配电网规划中的优化应用

Optimization application of an improved genetic algorithm in coal mine distribution network planning

  • 摘要: 针对传统遗传算法应用在矿区配电网规划中易发生早熟收敛从而影响规划的准确度问题,提出了一种多种群模拟退火遗传算法。该算法以年规划费用最小为目标函数,结合模拟退火算法的优点,同时加入多种群的特征,解决了配电网规划中易发生早熟收敛问题,提高了搜索效率,方便获得规划中全局最优解。实验结果表明,利用多种群模拟退火遗传算法对矿区配电网优化后,规划成本、种群迭代次数都有明显降低,运行时间比原有算法降低约6%,误差率降低约3%,更加实用高效。

     

    Abstract: In view of problems of premature convergence of application of traditional genetic algorithm in distribution network planning in mining area to affect accuracy of planning, a multi-population simulated annealing and genetic algorithm was proposed. The algorithm takes the minimum year planning cost as objective function , and combines with advantage of simulated annealing algorithm, adds multi-population characteristics at the same time, so as to solve the problem of premature convergence in power distribution network planning and improve search efficiency and convenient to obtain the global optimal solution in the planning. The experimental results show that cost planning, iteration times are significantly reduced by use of multi-population simulated annealing and genetic algorithm to optimize mine distribution network, the running time is reduced by about six percent, the error rate is reduced by about three percent compared with the original algorithm, and the algorithm is more effective and efficient.

     

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