Özet:

Metaheuristic algorithms, known as global optimization techniques, have been successfully used to solve a variety of complex and real optimization problems. Metaheuristic methods are inspired by different principles of physics, swarm intelligence, and biology. The Seahorse Optimization Algorithm (SOA) is a suggested swarm intelligence-based metaheuristic optimization algorithm inspired by the movement, hunting, and breeding behavior of seahorses in nature. Different methods have been proposed to achieve faster and higher accuracy convergence than metaheuristic optimization algorithms based on swarm intelligence. In this study, seven different chaotic maps, namely Chebyshev, Circle, Gauss, Iterative, Logistic, Piecewise, and Sine, were applied instead of random values in order to increase the convergence speed of SOA and to prevent it from getting stuck in local solutions. The Chaotic Seahorse Optimization Algorithm (CSOA), proposed for the first time in this study, has been applied to seven different benchmarking functions. Classic SOA was compared to evaluate the performance of the proposed CSOA. According to the experimental results, it was observed that CSOA achieved better results than classical SOA in seven different comparison functions.