一种种群自适应收敛的快速遗传算法

作为一种全局搜索算法,遗传算法的局部搜索能力较低,后期产生的无效进化与早熟收敛影响优化的速度和精度。已有的改进策略多以算法的时间复杂度为代价提高后期效率,严重限制了遗传算法在工业控制系统中的应用。针对这种情况,提出了一种新型种群自适应收敛的快速遗传算法,即通过提高种群的遗传质量,在严格控制算法复杂度的前提下提高优化性能。仿真结果证明,在不增加时间复杂度的前提下,新算法显著地提升了收敛精度和收敛速度。

Improved Genetic Algorithm with Adaptive Convergence Populations

The premature convergence seriously affects the performance of genetic algorithm. At present time, most of the improved algorithms focus on improving the convergence accuracy and speed at the expense of the algorithm time complexity, which limits the applications of genetic algorithm in industrial control system. For this situation, this paper presented a new improved genetic algorithm with adaptive convergence populations. This algorithm optimizes performance through increasing the genetic quality of populations, and at the same time, strictly controls the algorithm complexi ty. Simulation results show that the new algorithm can significantly improve the accuracy and speed of convergence, without time complexity increasing.