An adaptive chaotic sparrow search optimization algorithm

• Published in: 2021 IEEE 2nd International Conference on Big Data, Artificial Intelligence and Internet of Things Engineering (ICBAIE) pp. 76 - 82
• Main Authors: Chengtian, Ouyang, Yujia, Liu, Donglin, Zhu
• Format: Conference Proceeding
• Language: English
• Published: IEEE 26.03.2021
• Subjects: adaptive local search; Cauchy mutation; component; Convergence; Internet of Things; Optimization; optimization ability; Particle swarm optimization; Search problems; Sociology; Sparrow search algorithm; Statistics; Tent chaotic map
• DOI: 10.1109/ICBAIE52039.2021.9389888

Sparrow Search Algorithm (SSA) is a new type of swarm intelligence optimization algorithm recently proposed. Compared with other intelligent algorithms, this algorithm has better effect, but still has the problems of slow convergence speed, insufficient solution accuracy and easy to fall into local optimum. In response to these problems, an improved sparrow search algorithm (ISSA) is proposed. Based on the sparrow search algorithm, the improved Tent chaotic map is used to initialize the population to increase the diversity of the population and improve the global search ability; then an adaptive local search strategy is introduced to update the location of the discoverer and select the best location to enhance Local search ability to avoid the population from falling into the local optimum; next, the Cauchy mutation method is used to update the optimal individual position and find feasible solutions to improve the search accuracy and optimization ability; finally, through the simulation experiment of 8 benchmark functions and compare with three basic Comparing the algorithm with an improved sparrow algorithm, the results show that the ISSA algorithm has better results in convergence speed, solution accuracy and global optimization ability than the other four algorithms.