Tyrannosaurus optimization algorithm: A new nature-inspired meta-heuristic algorithm for solving optimal control problems

Abstract

Recently, the optimal control problem has gained much importance for solving practical problems. In this regard, the meta-heuristic algorithms are proven to be effective while solving these problems effectively and efficiently. However, these algorithms may not be effective for solving all the optimization problems as per the no free lunch theorem. Thus, there is always a scope of development of new meta-heuristic algorithms. This paper proposes a new hunting-based optimization algorithm called Tyrannosaurus (T-Rex) optimization algorithm (TROA). This algorithm is inspired by the hunting behavior of the T-Rex. This algorithm was tested on 12 benchmark problems and 4 practical optimal control problems. The performance of the TROA is compared with seven famous optimization techniques, i.e. Differential Evolution (DE) Algorithm, Particle Swarm Optimization (PSO), Grey Wolf Optimizer (GWO), White Shark Optimizer (WSO), Jellyfish Search (JS), Crow Search Algorithm (CSA), Golden Eagle Optimization (GEO). The results obtained for the proposed method have given better when compared to these methods.