融入小生境和混合变异策略的鲸鱼优化算法OA北大核心CSTPCD

As an advanced optimization algorithm with a simple structure,the whale optimization algorithm is used to solve problems in many disciplines.Through in-depth research on the whale optimization algorithm,it is found that the algorithm has problems such as slow convergence speed,inability to escape local optima,low convergence accuracy,and inability to balance global exploration and local exploitation capabilities.To address these issues,a whale optimization algorithm integrating niche and hybrid mutation strategy(NHWOA)is proposed.NHWOA introduces adaptive weights to balance the global exploration and local exploitation capabilities of the algorithm and accelerate its convergence speed.It divides the population into three niches of the same size,and independently optimizes them to increase population diversity.It uses a hybrid mutation strategy to randomly perturb the population,helping the algorithm escape local optima.Simula-tion experiments on the CEC2017 benchmark suite and application to feature selection problems validate the superiority and effectiveness of NHWOA.NHWOA exhibits faster convergence speed,higher convergence accuracy,and better robustness.