Recent Versions and Applications of Tunicate Swarm Algorithm

The Tunicate Swarm Algorithm (TSA) is a metaheuristic optimization method inspired by the navigation and feeding behaviors of marine tunicates, particularly their jet propulsion mechanics and swarm intelligence. TSA’s elegance lies in its core principles: collision avoidance through gravitational forces, optimal path identification via distance-based search, and swarm cohesion maintenance. Since its introduction in 2020, TSA has gained widespread attention for its simplicity, parameter efficiency, derivative-free operation, and robust convergence properties. This survey delves into TSA’s theoretical foundations and evolution, comprehensively reviewing its applications across diverse domains. A comparative study against six established algorithms on 23 benchmark functions highlights TSA’s superior performance. The algorithm has shown remarkable utility in fields such as computer science, engineering, and mathematics, experiencing exponential growth in adoption and citations. This review also explores TSA variants, including Chaotic TSA, Adaptive TSA, and hybrid approaches, analyzing their effectiveness across optimization challenges. Notable applications in power systems optimization, engineering design, medical image analysis, and network security are discussed with detailed insights into implementation strategies and performance metrics. Despite its strengths, TSA faces challenges in exploration and premature convergence on highly multimodal landscapes. The paper identifies promising research directions, such as quantum-inspired enhancements, distributed computing, and integration with Industry 4.0 technologies. This survey gives researchers and practitioners an in-depth understanding of TSA’s capabilities, limitations, and potential, positioning it as a transformative tool in computational intelligence and optimization.