Research for Distribution Systems Loss Minimization by Genetic Algorithm

本研究的目的是以下两个主题。(A)通过创建字符串结构和适应度函数等，建立遗传算法求解分布损失最小化问题的算法。(B)对算法进行改进，以便对大规模问题得到更精确的解。研究结果总结如下:(1)提出了用遗传算法求解配电损耗最小化问题的算法。(2)对于小规模问题，如果字符串位数不是很大，则可以通过所开发的算法获得精确（接近最优）的解。计算时间比模拟退火法缩短1/6。对于第二个目标(B)，研究了字符串结构、适应度函数、交叉率、突变率、交叉点数量、人工交叉、初始字符串模式等对计算结果的影响。调查结果如下：(3)每个特定问题都存在最优交叉率和最优突变率。通常，它们分别为0.6-0.8和1 - 2个字符串的1位。(4)对于大型系统，只有初始字符串中存在有效（有利于损失最小结构）的模式，才能得到准确的计算结果。(5)其他人工操作对效果无明显影响。从以上结果可以看出，目标(A)和(B)都达到了。以上结果发表在下一页所示的论文中。

The objective of this research is the following two subjects.(A) To establish a solution algorithm of the distribution loss minimization problem by the genetic algorithm through creating the string structure and the fitness function, etc.(B) To improve the algorithm so as to get more accurate solutions for large scale problems. The research results are summarized as follows.(1) The solution algorithm of the distribution loss minimization problem by the genetic algorithm is developed.(2) For a small scale problem, if the number of string bits is not so large, an accurate (near optimal) solution can be available through the developed algorithm. The computation time is 1/6 times smaller than that by simulated annealing method. For the second objective, (B), how the string structure, fitness function, crossover rate, mutation rate, number of crossover points, artificial crossovers, initial string patterns, etc. affect the calculation results is investigated. The investigated results are as follows.(3) The optimal crossover rate and mutation rate exist for each specific problem. Ordinarily, they are 0.6-0.8 and one bit for 1 - 2 string(s) respectively.(4) For a large system, an accurate calculation result can be available only when effective (good for loss minimum structure) schema exist in the initial strings.(5) Other artificial operations do not affect the result significantly. From the above result, it can be seen that objectives (A) and (B) are achieved. The above results are published in the papers shown in the next page.