基于空间衰减自扩散机制的黏菌遗传混合算法OA北大核心

According to the imbalance between exploration and exploitation,susceptibility to local optima,and low search efficiency of metaheuristic algorithms,a hybrid slime mould genetic algorithm based on spatial attenuation self-diffusion mechanism if presented.The algorithm uses genetic algorithm as the basic structure,and guides individuals to search in the solution space by recombining features through three operations:selection,crossover,and mutation.Firstly,it introduces oscillation-contraction mechanism with characteristics of both positive-negative feedback and random walking as crossover operators to enhance both global and local search capabilities.Secondly,a self-diffusion mechanism based on spatial decay is proposed as a mutation operator.This mechanism guides the diffusion motion using a spatial scale which decreases over the algorithm's lifecycle,promoting diversity in the early stages and effective exploration of neighborhood information in the later stages.Finally,a discriminative control strategy is introduced to adaptively adjust the algorithm's parameters based on the distribution deviation of the population fitness.This strategy helps balance the exploration and exploitation capabilities of the algorithm.To validate the algorithm's performance,experiments are conducted on two publicly available benchmark test sets:IEEE CEC2017 and IEEE CEC2021.The results demonstrate that the proposed algorithm effectively balances exploration and exploitation capabilities and exhibits superior optimization performance compared with other 23 different types of algorithms.