To address the issues of slow convergence and susceptibility to local optima in traditional differential evolution algorithms,as well as the poor optimization stability caused by the randomness in individual selection,a multi-restart strategy is introduced in this paper.The algorithm is executed multiple times with different random seeds,increasing the algorithm's spatial exploratory capability and,to a certain extent,resolving the problem of easily falling into local optima.Through the incorporation of a new mutation strategy,the optimization stability is improved by approximately 10%.Additionally,a parameter self-adaptive tuning mechanism is introduced,dynamically adjusting the algorithm's parameter values,resulting in an approximately 10%increase in convergence speed and enhancing the algorithm's robustness.
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