Improved sandcat swarm optimization algorithm for solving global optimum problems

The sand cat swarm optimization algorithm (SCSO) is a metaheuristic algorithm proposed by Amir Seyyedabbasi et al. SCSO algorithm mimics the predatory behavior of sand cats, which gives the algorithm a strong optimized performance. However, as the number of iterations of the algorithm increases, the moving efficiency of the sand cat decreases, resulting in the decline of search ability. The convergence speed of the algorithm gradually decreases, and it is easy to fall into local optimum, and it is difficult to find a better solution. In order to improve the search and movement efficiency of the sand cat, and enhance the global optimization ability and convergence performance of the algorithm, an improved sand cat Swarm Optimization (ISCSO) algorithm was proposed. In ISCSO algorithm, we propose a low-frequency noise search strategy and a spiral contraction walking strategy according to the habit of sand cat, and add random opposition-based learning and restart strategy. The frequency factor was used to control the search direction of the sand cat, and the spiral contraction hunting was carried out, which effectively improved the randomness of the population, expanded the search range of the algorithm, enhanced the moving efficiency of the sand cat, and accelerated the convergence speed of the algorithm. We use 23 standard benchmark functions and IEEE CEC2014 benchmark functions to compare ISCSO with 10 algorithms, and prove the effectiveness of the improved strategy. Finally, ISCSO was evaluated using five constrained engineering design problems. In the results of these problems, using ISCSO has 3.08%, 0.23%, 0.37%, 22.34%, 1.38% improvement compared with the original algorithm respectively, which proves the effectiveness of the improved strategy in practical application problems. The source code website for ISCSO is https://github.com/Ruiruiz30/ISCSO-s-code.