Aiming at overcoming drawbacks of lower accuracy and being trapped easily into local optimums of the sparrow search algorithm(SSA), this paper proposed an enhanced sparrow search algorithm by integrating niche mechanism(NSSA). Firstly, NSSA adopted an adaptive niche mechanism based on producers' similarity to automatically partition the whole sparrow population into several producer-centered speciation, and to move scroungers inside the speciation towards to the producer inside the same speciation, in order to maintaining the diversity of the whole population. Secondly, NSSA applied a fuzzy logic based differential mutation operation to further mutate sparrow producers to raise the probability to jump out of local optimums. It used 12 functions selected from the CEC2017 benchmark function as the testbed to evaluate NSSA with CSSOA, ISSA, MSSSA, SHSSA, and MSWOA. Experimental results show that NSSA even can raise convergence accuracy up by 10% compared with the second ranked evaluated algorithm on most of multimodal objective test functions. This paper also conducted ablation experiments for each improved strategy proposed in NSSA. Experimental results show NSSA can achieve supreme performance by applying all 3 strategies simultaneously. At last, NSSA is applied to solve the pressure vessel design problem. Experimental results illustrate that NSSA has the ability to find the best solutions compared to other evaluated algorithms.

[2023-12-11] Accepted Paper /doc/2023.08.0353/v1

[2024-04-05] Printed Article /doc/2023.08.0353/v2