Optimization of free surface measurement path based on improved differential evolution algorithm

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.