Chaotic artificial hummingbird algorithms

Optimization has become an indispensable field in today's world. The main reason for this is that it is fast and has a high success rate in solving problems in Physics, Chemistry, Engineering and many other fields. While doing this job, optimization algorithms have independent variables and try to converge to the optimum solution with various restrictive conditions. The unique abilities of living beings in nature such as feeding, hunting, flying, and attacking, and their flawless solidarity with each other, have inspired the hierarchical structure optimization algorithms within them. In our thesis, YSA, BA, KAA, YA, GKA, which were created by taking inspiration from this unique solidarity of living beings, were examined. These are Tent map, Gauss map, Circle map, Logistic Map, Henon map, Sinusidal map, respectively.. Metaheuristic algorithms use randomness to diversify the search process and escape from local minima. In our thesis, instead of this classical starting point, which involves a lot of time and a lot of randomness, chaotic maps were used. Six new versions of the artificial hummingbird optimization algorithm with chaotic maps were produced to increase its performance and achievements. These are Tent map, Gaussian map, Circle map, Logistic map, Henon map, Sinusoidal map, respectively. The achievements of these new algorithms were measured with the help of ten single-mode and multi-mode quality test functions. According to the results we obtained in the conclusion section, it was seen that these six versions of KYSA were more successful in converging to the global optimum and gave faster results than other algorithms.