Adaptive Simplified Chicken Swarm Optimization Based on Inverted S-Shaped Inertia Weight

Considering the issues of premature convergence and low solution accuracy in solving high-dimensional problems with the basic chicken swarm optimization algorithm, an adaptive simplified chicken swarm optimization algorithm based on inverted S-shaped inertia weight (ASCSO-S) is proposed. Firstly, a simplified chicken swarm optimization algorithm is presented by removing all the chicks from the chicken swarm. Secondly, an inverted S-shaped inertia weight is designed and introduced into the updating process of the roosters and hens to dynamically adjust their moving step size and thus to improve the convergence speed and solution accuracy of the algorithm. Thirdly, in order to enhance the exploration ability of the algorithm, an adaptive updating strategy is added to the updating process of the hens. Simulation experiments on 21 classical test functions show that ASCSO-S is superior to the other comparison algorithms in terms of convergence speed, solution accuracy, and solution stability. In addition, ASCSO-S is applied to the parameter estimation of Richards model, and the test results indicate that ASCSO-S has the best fitting results compared with other three algorithms.

Automated summary

This paper introduces an adaptive simplified chicken swarm optimization algorithm based on inverted S-shaped inertia weight to solve the problems of premature convergence and low solution accuracy in high-dimensional problems. By removing the chicks, introducing the inverted S-shaped inertia weight, and adding an adaptive updating strategy, the algorithm outperforms other algorithms in terms of convergence speed, solution accuracy, and solution stability.