RUNge Kutta Optimizer (RUN) – A Novel Metaphor-Free Population-Based Optimization Method
The optimization field is constantly evolving, with researchers developing new and advanced algorithms to solve complex problems. However, some of these algorithms do not contribute much to the optimization process but rely on metaphors and mimic animals' searching trends. These clichéd methods suffer from locally efficient performance, biased verification methods, and high similarity between their components' interactions.
This study proposes a solution to go beyond these traps of metaphors and introduce a novel metaphor-free population-based optimization method based on mathematical foundations and ideas of the Runge Kutta (RK) method. The proposed RUNge Kutta optimizer (RUN) uses the logic of slope variations computed by the RK method as a promising and logical searching mechanism for global optimization.
The RUN Algorithm’s Working
The RUN algorithm has two active exploration and exploitation phases for exploring feature space's promising regions and constructively moving towards the global best solution. Furthermore, an enhanced solution quality (ESQ) mechanism is employed to avoid local optimal solutions and increase convergence speed.
The RUN algorithm's efficiency was evaluated by comparing it with other metaheuristic algorithms in 50 mathematical test functions and four real-world engineering problems. The RUN algorithm showed superior exploration and exploitation tendencies, fast convergence rates, and local optima avoidance. In optimizing the constrained engineering problems, the metaphor-free RUN demonstrated its satisfactory performance.
The Importance of RUN Algorithm in Optimization Problems
The RUN algorithm's importance can be understood by looking at its simple yet effective working. The algorithm was designed to deal with various types of optimization problems globally. The mathematical foundation behind the algorithm is the Runge Kutta method, which is well known in mathematics for its accuracy and efficiency in solving differential equations.
Furthermore, the RUN algorithm's metaphor-free approach sets it apart from the rest of the optimization algorithms that rely heavily on animal behavior metaphors. These metaphors have limitations and are not suitable for solving complex optimization problems that require a logical and mathematical approach.
The RUN algorithm benefits from the ESQ mechanism, which helps the algorithm to avoid local optima and increase convergence speed. This mechanism allows the algorithm to explore more promising regions of the feature space and find the global best solution.
The RUNge Kutta optimizer (RUN) is a promising new approach to solving complex optimization problems. It is based on the mathematical foundations of Runge Kutta method and is a metaphor-free optimization algorithm. The RUN algorithm's efficiency and effectiveness were evaluated by comparing it with other metaheuristic algorithms in various test functions and engineering problems, and it showed superior performance.
Therefore, the RUN algorithm is a suitable tool for real-world optimization problems and can be used by researchers in different fields to solve complex optimization problems. The authors invite the community for extensive evaluations of this deep-rooted optimizer as a promising tool for real-world optimization.
