Convergence analysis and application of an improved sparrow search algorithm
Aiming at the problems of local optimization and slow convergence speed of sparrow search algorithms,an improved sparrow search algorithm(ISSA)is proposed.Firstly,a good point set method is used to initialize the sparrow population,which increases the population diversity and improves the convergence speed and accuracy of the algorithm.Then,the golden sine algorithm is used to optimize the founder's position update process to further balance the global exploration and local development capabilities of the algorithm.Finally,the Levy flight algorithm is used to optimize the follower's position update process,expand its search space,and improve the problem that it is easy to fall into local optimization.By establishing the Markov chain model,the convergence of the improved algorithm is proved from the theoretical perspective.Five standard test functions and other classical swarm intelligent optimization algorithms are selected to verify the effectiveness of the improved algorithm from the perspective of simulation experiments.The improved algorithm is used to optimize the variational mode decomposition(VMD)parameters and echo state network(ESN)parameters.The ISSA-VMD-ESN model is constructed and applied to short-term electricity price prediction,and the superiority of the improved algorithm is further verified by simulation experiments.
sparrow search algorithmgood point set methodgolden sine algorithmLevy flightastringency analysiselectricity price forecast
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