Tunicate Swarm Algorithm: A new bio-inspired based metaheuristic paradigm for global optimization
By Satnam Kaur, L. Awasthi, A. L. Sangal, Gaurav Dhiman. Engineering applications of artificial intelligence, 2020
1,106 citations recorded by the source corpus.
Abstract This paper introduces a bio-inspired metaheuristic optimization algorithm named Tunicate Swarm Algorithm (TSA). The proposed algorithm imitates jet propulsion and swarm behaviors of tunicates during the navigation and foraging process. The performance of TSA is evaluated on seventy-four benchmark test problems employing sensitivity, convergence and scalability analysis along with ANOVA test. The efficacy of this algorithm is further compared with several well-regarded metaheuristic approaches based on the generated optimal solutions. In addition, we also executed the proposed algorithm on six constrained and one unconstrained engineering design problems to further verify its robustness. The simulation results demonstrate that TSA generates better optimal solutions in comparison to other competitive algorithms and is capable of solving real case studies having unknown search spaces. Note that the source codes of the proposed TSA algorithm are available at
Subfields: Artificial Intelligence, Computational Theory and Mathematics
View Paper: https://www.semanticscholar.org/paper/f67224478e60fe8ab34bdfe0f5de95059fc45e68
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Citations in the corpus
- Tuna Swarm Optimization: A Novel Swarm-Based Metaheuristic Algorithm for Global Optimization (2021, methodology): “TSO vs HHO EO TSA GWO SSA PSO WOA Function p Win p Win p Win p Win p Win p Win p Win F1 1.21E− 12 + 1.21E− 12 + 1.21E− 12 + 1.21E− 12 + 1.21E− 12 + 1.21E− 12 + 1.21E− 12 + F2 3.02E− 11 + 3.02E− 11 + 3.02E− 11 + 3.02E− 11 + 3.02E− 11 + 3.02E− 11 + 3.02E− 11 + F3 1.21E− 12 + 1.21E− 12 + 1.21E− 12 + 1.”
- Puma optimizer (PO): a novel metaheuristic optimization algorithm and its application in machine learning (2024, neutral): “TSA[67] Parameter P min 1 Parameter P max 4 DMOA[66] bb 3 Spiral factor b 1”
- Pelican Optimization Algorithm: A Novel Nature-Inspired Algorithm for Engineering Applications (2022, methodology): “In TSA, the population is updated based on four phases including avoiding conflicts between search agents, moving towards the best neighbor, converging towards the best search agent, and swarm behavior [17].”
- A novel algorithm for global optimization: Rat Swarm Optimizer (2020, neutral): “…2019; Dhiman, 2019a, 2019c; Dehghani, Montazeri, Malik, Dhiman, & Kumar, 2019; Maini & Dhiman, 2018; Pallavi & Dhiman, 2018; Garg & Dhiman, 2020; S. Kaur, Awasthi, Sangal, & Dhiman, 2020) i i i inexpensive, flexible, i i i (Ragmani, Elomri, Abghour, Moussaid, & Rida, 2019; D. Yang, Wang, Tian, &…”
- Zebra Optimization Algorithm: A New Bio-inspired Optimization Algorithm for Solving Optimization Algorithm (2022, methodology): “The use of foraging and nutritional behavior modeling led to the design of metaheuristic algorithms such as Tunicate Swarm Algorithm (TSA) [14], Raccoon Optimization Algorithm (ROA) [15], and Salp Swarm Algorithm (SSA) [16].”
- Northern Goshawk Optimization: A New Swarm-Based Algorithm for Solving Optimization Problems (2021, background): “Some other swarm-based algorithms are Raccoon Optimization Algorithm (ROA) [11], Teaching-Learning Based Optimization (TLBO) [12], Crow Search Algorithm (CSA) [13], Grasshopper Optimization Algorithm (GOA) [14], Tunicate Swarm Algorithm (TSA) [15], and Marine Predators Algorithm (MPA) [16].”