Analyzing the Performance of the Red Deer Optimization Algorithm in Comparison to Other Metaheuristic Algorithms

This study performs a thorough comparative analysis of the Red Deer Optimization Algorithm (RDOA) in comparison to five well-established metaheuristic algorithms: Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Differential Evolution (DE), Artificial Bee Colony (ABC), and Whale Optimization Algorithm (WOA). The main objective is to evaluate the performance of RDOA on a range of benchmark problems, including essential unimodal and sophisticated multimodal functions. The methodology incorporates hyperparameters optimization for each algorithm to optimize performance and assesses them on six standard benchmark problems (Sphere, Rosenbrock, Bohachevsky, Griewank, Rastrigin, and Eggholder). Convergence plots are examined to demonstrate the rate at which convergence occurs and the level of stability achieved. The results demonstrate that RDOA performs well compared to other algorithms in all benchmarks and excels in dealing with multimodal functions. However, the selection of an algorithm should be based on the specific characteristics of the problem, taking into account their distinct advantages.

کلیدواژه ها:

Optimization ، Metaheuristic ، Nature Inspired ، Red Deer Optimization Algorithm ، RDOA