CLTSA: A Novel Tunicate Swarm Algorithm Based on Chaotic-Lévy Flight Strategy for Solving Optimization Problems

• Published in: Mathematics (Basel) Vol. 10; no. 18; p. 3405
• Main Authors: Cui, Yi, Shi, Ronghua, Dong, Jian
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.09.2022
• Subjects: Algorithms; Back propagation; Behavior; benchmark test functions; Benchmarks; Chaos theory; chaotic mapping; Design engineering; engineering design problems; Exploitation; Food science; Genetic algorithms; Heuristic; Heuristic methods; Lévy flight strategy; Mathematical optimization; meta-heuristic; Neural networks; Optimization; Optimization algorithms; Predation; Search algorithms; Swarm intelligence; Tests, problems and exercises; tunicate swarm algorithm; China
• ISSN: 2227-7390; 2227-7390
• DOI: 10.3390/math10183405

In this paper, we proposed a tunicate swarm algorithm based on Tent-Lévy flight (TLTSA) to avoid converging prematurely or failing to escape from a local optimal solution. First, we combined nine chaotic maps with the Lévy flight strategy to obtain nine different TSAs based on a Chaotic-Lévy flight strategy (CLTSA). Experimental results demonstrated that a TSA based on Tent-Lévy flight (TLTSA) performed the best among nine CLTSAs. Afterwards, the TLTSA was selected for comparative research with other well-known meta-heuristic algorithms. The 16 unimodal benchmark functions, 14 multimodal benchmark functions, 6 fixed-dimension functions, and 3 constrained practical problems in engineering were selected to verify the performance of TLTSA. The results of the test functions suggested that the TLTSA was better than the TSA and other algorithms in searching for global optimal solutions because of its excellent exploration and exploitation capabilities. Finally, the engineering experiments also demonstrated that a TLTSA solved constrained practical engineering problems more effectively.