Abstract: The Tunicate Swarm Algorithm (TSA) inspires by simulating the lives of Tunicates at sea and how food is obtained. This algorithm is easily entrapped to local optimization despite the simplicity and optimal, leading to early convergence compared to some metaheuristic algorithms. This paper sought to improve this algorithm's performance using mutating operators such as the lévy mutation operator, the Cauchy mutation operator, and the Gaussian mutation operator for global optimization problems. Thus, we introduced a version of this algorithm called the QLGCTSA algorithm. Each of these operators has a different performance, increasing the QLGCTSA algorithm performance at a specific optimization operation stage. This algorithm has been run on benchmark functions, including three different compositions, unimodal (UM), and multimodal (MM) groups and its performance evaluate six large-scale engineering problems. Experimental results show that the QLGCTSA algorithm had outperformed other competing optimization algorithms.

Key words: Tunicate swarm algorithm (TSA) , · Mutation strategy , · Global optimization