This paper comprehensively analyzes the Manta Ray Foraging Optimization (MRFO)
algorithm and its integration into diverse academic fields. Introduced in 2020, the MRFO …

Metaheuristic algorithms are an important area of research in artificial intelligence. The
tumbleweed optimization algorithm (TOA) is the newest metaheuristic optimization algorithm …

Metaheuristic algorithms are currently being studied in depth by many scholars, and it is an
important task to improve the learning and adaptive capabilities of the algorithms so that …

In this article, a chaotic computing paradigm is investigated for the parameter estimation of
the autoregressive exogenous (ARX) model by exploiting the optimization knacks of an …

Aquila Optimizer (AO) and Artificial Rabbits Optimization (ARO) are two recently developed
meta-heuristic optimization algorithms. Although AO has powerful exploration capability, it …

As the complexity of optimization problems continues to rise, the demand for high-
performance algorithms becomes increasingly urgent. This paper addresses the challenges …

To address the shortcomings of the sine cosine algorithm such as the low search accuracy,
slow convergence speed, and easily falling into local optimality, a sine cosine algorithm for …

Monitoring the energy generated by a solar system based on various weather conditions
requires an accurate forecast algorithm. In this research, a new deep learning method called …

Metaheuristic algorithms are often preferred for solving constrained engineering design
optimization problems. The most important reason for choosing these algorithms is that they …

Metaheuristic algorithms are often preferred for solving constrained engineering design
optimization problems. The most important reason for choosing these algorithms is that they …