基于多约束条件的改进遗传算法路径规划

Improved Genetic Algorithm Route Planning Based on Multiple Constraints

变电站位置确定是一个典型的最短路径问题,在实际处理中需要考虑功率、负载、损耗等多个因素。将不同元启发式算法和搜索方法中的元素进行混合,提出一种基于多约束条件的改进遗传算法,用于解决上述路径规划问题。使用相同例子对不同算法进行模拟仿真,得出蚁群成本平均为15.102 4s,代数为21,适应值为0.007 913 475 3。改进的遗传成本为19.234 7s,代数为38,适应值为0.014 756 211。模拟退火成本为36.493 3s,代数为47,适应值为0.017 414 562 4。标准遗传成本34.253 7s,代数为68,适应值为0.019 527 878 1。以上数据证明改进的遗传算法在搜索效率、收敛速度和最终结果上具有一定优势。

Substation location determination is a typical shortest path problem.In path-designing,it should consider additional information such as short circuit currents or powers,generation and load data,inertia,grid losses and so on.In order to solve this complex multi-objective combinatorial optimization problem,this paper mixes the different Meta heuristics with the elements in the search method,proposes an improved genetic algorithm based on multi-constraint conditions,and uses the same example for different algorithms.Simulation simulations show that the average ant colony cost is 15.102 4 seconds and the algebra is 21,and the adaptation value is 0.007 913 475 3.The improved genetic cost was 19.234 7 seconds,the algebra was 38,and the fitness value was 0.014 756 211.The simulated annealing cost was 36.493 3 seconds and the algebra was 47.The fitness value was 0.017 414562 4.The standard genetic cost was 34.253 7 seconds,the algebra was 68,and the fitness value was 0.0195278781.The experimental results show that the improved genetic algorithm has some advantages in the multi-constraint condition,the search efficiency and the convergence speed.