Improved Differential Evolution Algorithm Based on Time-Space Joint Denoising

In the optimization process of solving engineering problems,the evaluation of individual fitness may be affected by environmental noise,so as to affect the reasonable survival of the fittest operation on the population,and result in a decline in algorithm performance.In order to combat the impact of noise environment,an improved differential evolution algorithm(SEDADE) based on joint temporal and spatial denoising is proposed.The population is divided into two subpopulations according to fitness ranking,and the subpopulations composed of poorly evaluated individuals are evolved using a distribution estimation algorithm(EDA).Gaussian distribution is used to model the solution space,using the randomness of multiple individual noises in the solution space to offset the noise impact.Differential evolution algorithm(DE) is used to evolve subpopulations with better evaluated individual composition,and a time-based stagnation resampling mechanism is introduced to denoise to improve convergence accuracy.The EDA information utilization operation based on probability selection is performed on the two subpopulations derived from time-space mixed evolution,and the global information obtained from EDA search is used to guide the search direction of DE to avoid falling into local optimization.In the experiment,a benchmark function interfered by zero mean Gaussian noise is used,and it is found that SEDADE is competitive with other algorithms.In addition,the effectiveness and rationality of the proposed mechanism are verified through ablation experiments.