To address the issues with inadequate search space, sluggish convergence and easy fall into local optimality during iteration of the sparrow search algorithm (SSA), a multi-strategy improved sparrow search algorithm (...
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To address the issues with inadequate search space, sluggish convergence and easy fall into local optimality during iteration of the sparrow search algorithm (SSA), a multi-strategy improved sparrow search algorithm (ISSA), is developed. First, the population dynamic adjustment strategy is carried out to restrict the amount of sparrow population discoverers and joiners. Second, the update strategy in the mining phase of the honeypot optimization algorithm (HBA) is combined to change the update formula of the joiner's position to enhance the global exploration ability of the algorithm. Finally, the optimal position of population discoverers is perturbed using the perturbation operator and levy flight strategy to improve the ability of the algorithm to jump out of local optimum. The experimental simulations are put up against the basic sparrow search algorithm and the other four swarm intelligence (SI) algorithms in 13 benchmark test functions, and the Wilcoxon rank sum test is used to determine whether the algorithm is significantly different from the other algorithms. The results show that the improved sparrow search algorithm has better convergence and solution accuracy, and the global optimization ability is greatly improved. When the proposed algorithm is used in pilot optimization in channel estimation, the bit error rate is greatly improved, which shows the superiority of the proposed algorithm in engineering application.
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