改进的混合蛙跳算法性能分析及其在电力系统经济调度中的应用

针对基本混合蛙跳算法收敛速度慢,优化精度低的问题,提出了改进的混合蛙跳算法:通过引入自适应因子,保持了算法开发与探索的平衡,维持了种群的多样性,提高了个体向局部最优或全局最优个体学习的能力,加快了算法的收敛速度。通过对4个测试函数和电力系统中经济调度问题进行优化实验,并与基本混合蛙跳算法和相关文献中的改进算法进行比较,实验结果表明所提出的改进算法取得了更加理想的运算结果,具有更好的优化性能。

Performance analysis of improved SFLA and the application in economic dispatch of power system

Aiming at the problems of the shuffled frog leaping algorithm (SFLA), such as slow convergence speed and low optimization precision, the improved SFLA is proposed. Through introducing the adaptive factor, we ensure the algorithm balance between exploration and development, maintain the population diversity, improve the capacity of the individual learning from the best individual of local optimization or the global optimum, and speed up the convergence rate. Through testing four benchmark functions and optimizing the problems of economic dispatch in the power system, and comparing with basic SFLA and the improved SFLA in related references, we find that the improved SFLA achieves more reasonable results and has better optimization performance.