Algorithm "Sea Jellyfish ": Utilizing Artificial Intelligence and Nature-Inspired Approaches in Solving Optimization Problems

The metaheuristic algorithms are optimization approaches used to solve complex problems with large search spaces. These algorithms have gained significant attention due to their efficiency and applicability in real-world problems. Inspired by biological phenomena, they utilize optimization techniques and perform well in complex problems by not relying on the gradient of the objective function. Metaheuristic algorithms fall into categories such as evolutionary algorithms, swarm intelligence, and exploratory algorithms, drawing inspiration from the behavior of living organisms. Extensive research in the field of metaheuristic algorithms has demonstrated their high importance in optimization problem-solving. However, achieving a proper balance between exploration and exploitation stages in metaheuristic algorithms is challenging due to their stochastic nature. In this study, an algorithm called "Jellyfish Search (JS)" is proposed, which draws inspiration from the collective behavior of jellyfishes in the depths of the ocean. The advantages of this algorithm include the collective tracking of jellyfishes in the ocean currents, their movement in congestion (including active and inactive movements), and temporal control to switch between these movements. By achieving a proper balance between exploration and exploitation, along with a time control mechanism and the use of chaotic mapping to improve the diversity of the initial population, this algorithm leads to better results.