An Improved Tunicate Swarm Algorithm with Best-random Mutation Strategy for Global Optimization Problems

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 levy 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.

Automated summary

This paper introduces the Tunicate Swarm Algorithm (TSA) and its improved version QLGCTSA algorithm, which uses different mutating operators to enhance the algorithm's performance. Experimental results show that the QLGCTSA algorithm outperforms other competing optimization algorithms on various optimization problems.