An Improved Adaptive Shuffled Frog Leaping Algorithm to solve various non-smooth Economic Dispatch problems in power systems

This paper presents an Improved Adaptive Shuffled Frog Leaping Algorithm (IASFLA) applied to solve practical Economic Dispatch (ED) problem that is known as a highly constrained non-linear optimization problem with nonconvex space. In this paper, a new adaptive frog leaping rule is suggested to enhance the local exploration and performance of the SFLA. Furthermore, to improve the performance and quicker algorithm convergence, the genetic mutation operator is employed for new frog generation instead of random frog generation. To show the efficiency of the proposed IASFLA in solving ED problem, it is applied to four test systems having nonsmooth and non-convex solution spaces. In order to validate the obtained results by proposed IASFLA, an ordinary SFLA and a modified SFLA (MSFLA) are adopted from the literature and applied for comparison. Also, the results obtained by the SFL algorithms are compared with the recently previous methods reported in the literature. Simulation studies show the superiority and advantages of the proposed IASFLA over the other methods.