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为克服蚁群算法前期收敛慢、易陷入局部最优解且参数难以确定的缺点,提出了遗传-蚁群算法进行库群长期优化调度问题的求解。该方法应用遗传算法生成问题的初始解,并将初始解的适应度转化为蚁群算法的信息素初始值,同时引入遗传算法染色体交叉、变异的思想进行蚁群算法参数最优组合的确定,提高了蚁群算法的优化性能和求解精度。对乌江流域4座水电站的计算结果表明,该算法可显著改善优化结果质量,获得良好的调度方案,是求解库群长期优化调度问题的一种有效方法。
In order to overcome the shortcomings that the ant colony algorithm converges slowly and is easy to fall into the local optimal solution and the parameters are difficult to be determined in the early stage, a genetic-ant colony algorithm is proposed to solve the problem of long-term optimal scheduling of the group. The method uses genetic algorithm to generate the initial solution of the problem and transforms the initial solution to the initial value of the pheromone of ant colony algorithm. At the same time, the idea of genetic algorithm crossover and mutation is introduced to determine the optimal combination of ant colony algorithm parameters. Improve the performance of ant colony algorithm optimization and solution accuracy. The calculation results of 4 hydropower stations in Wujiang River Basin show that this algorithm can significantly improve the quality of the optimization results and obtain a good scheduling scheme, which is an effective way to solve the long-term optimal scheduling of the reservoir group.