Snake Optimizer for Large-Scale Optimizaton Problems

Abstract

The Snake Optimizer (SO) is a newly proposed heuristic algorithm in recent years. It was proposed in the original paper for continuous optimization problems. When the literature was reviewed, it was noticed that the success of SO for large-sized problems was not tested. In this study, the success of SO was examined on data sets consisting of six different large-sized (1024, 3072, and 4868) EEG signals, known as the big data optimization problem. The success of SO has been thoroughly investigated on a big data optimization problem in three different iterations (100, 300, and 500) and three different population sizes (30, 50, and 100). The convergence graphs of the problem datasets according to the population size were drawn and examined. SO was run independently twenty times for each dataset. Statistical evaluations such as average, standard deviation, best, worst, and time were made on the results obtained. According to the average results, the population size and the maximum number of iterations have a direct effect on the result, but they also increase the solution time of the problem. SO has been compared with various heuristic algorithms selected from the literature (Jaya, AOA, BA, PSO-Q, and IPSO-Q). According to the results, SO achieved better results in all big data optimization problems. The results showed that the SO heuristic algorithm was able to maintain its success as the size of the problem increased. This comes from SO's ability to explore locally and globally. According to the results, SO is a heuristic algorithm with strong exploration and exploitation capabilities and can be chosen as an alternative algorithm for large-size continuous optimization problems.