In this study, a new hybrid metaheuristic algorithm named Chaotic Sand Cat Swarm Optimization (CSCSO) is proposed for constrained and complex optimization problems. This algorithm
combines the features of the recently introduced SCSO with the concept of chaos. The basic aim of
the proposed algorithm is to integrate the chaos feature of non-recurring locations into SCSO’s core
search process to improve global search performance and convergence behavior. Thus, randomness
in SCSO can be replaced by a chaotic map due to similar randomness features with better statistical
and dynamic properties. In addition to these advantages, low search consistency, local optimum trap,
inefficiency search, and low population diversity issues are also provided. In the proposed CSCSO,
several chaotic maps are implemented for more efficient behavior in the exploration and exploitation
phases. Experiments are conducted on a wide variety of well-known test functions to increase the
reliability of the results, as well as real-world problems. In this study, the proposed algorithm was
applied to a total of 39 functions and multidisciplinary problems. It found 76.3% better responses
compared to a best-developed SCSO variant and other chaotic-based metaheuristics tested. This
extensive experiment indicates that the CSCSO algorithm excels in providing acceptable results
