An Improved Sand Cat Swarm Optimization with Lens Opposition-based Learning and Sparrow Search Algorithm

Abstract

In order to enhance the global search ability of the sand cat swarm optimization, avoid falling into the local optimum at a later stage, and improve the performance of the algorithm, an improved algorithm is proposed - Improved sand cat swarm optimization based on lens opposition-based learning and sparrow search algorithm (LSSCSO). A dynamic spiral search is introduced in the exploitation stage to make the algorithm search for better positions in the search space and improve the convergence accuracy of the algorithm. The lens opposition-based learning and the sparrow search algorithm are introduced in the later stages of the algorithm to make the algorithm jump out of the local optimum and improve the global search capability of the algorithm. To evaluate the effectiveness of LSSCSO in solving global optimization problems, the performance of the algorithm is tested using 23 standard benchmark functions and compared with seven competitive algorithms, which show that LSSCSO has strong optimality finding ability and performs optimally in most cases. Finally, the application of LSSCSO to four engineering optimization problems also verifies the effectiveness of the algorithm in solving engineering optimization problems.