A health-based metaheuristic algorithm: Cholesterol algorithm

Optimization is as old as the history of the universe. It can be seen anywhere from the farthest point of the universe to the atomic particle. Optimization can sometimes be seen in an engineering design and sometimes in an ant's search for food. The main purpose of all is to find the best possible result under certain conditions. The universe, nature, animals solve their problems easily by instinct. For this reason, people try to solve complex problems by imitating the universe and nature. They developed metaheuristics to solve complex optimization problems. There are many algorithms in the literature that are inspired by animals and nature. However, the number of algorithms inspired by the human body such as artificial neural networks and artificial immune systems are very few. In this thesis, a new health-based metaheuristic algorithm has been developed, inspired by the cholesterol metabolism in the human body. The cholesterol algorithm is modeled using the cholesterol levels in the human body. This thesis focuses on the performance of the cholesterol algorithm on unconstrained continuous optimization problems. By keeping the structure of the cholesterol algorithm simple, a faster and more flexible algorithm has been developed. For the performance analysis of the cholesterol algorithm, 23 comparison tests were used and the test results were compared with 11 different algorithms.