Tactical flight optimizer: a novel optimization technique tested on mathematical, mechanical, and structural optimization problems

The current study presents a novel gradient-free metaheuristic search algorithm named Tactical Flight Optimizer (TFO), tailored to meet the growing need for high-performance optimization techniques in solving complex engineering and mathematical problems. The main contribution of this study is the development of a method that simulates tactical air combat formations, offering a sophisticated alternative to conventional search algorithms. In the proposed method, the location of each agent is updated based on a resultant vector derived from three updating vectors. The updating vectors incorporate total information stored by the agents in each iteration. Consequently, the navigation process is guided by a more logical mechanism rather than a simple random process. The search performance of the TFO is initially benchmarked by solving a set of constrained mathematical functions. Subsequently, it is evaluated by addressing a suite of constrained mechanical and structural problems, containing both discrete and continuous decision variables. The obtained results are compared with five other well-stablished metaheuristic techniques. Acquired numerical results indicate that the TFO algorithm can provide promising results in solving both mathematical and engineering problems in terms of computational cost, accuracy, and stability.