Artificial Afterimage Algorithm: A New Bio-Inspired Metaheuristic Algorithm and Its Clustering Application

Abstract

Metaheuristic methods are optimization methods that look for different ways to converge to a solution to a problem where it is difficult to find a solution analytically. Their difference from known optimization methods is that they imitate living things or systems in nature. Each metaheuristic method has its equations, and the solution is found using these equations. In this study, a new, metaheuristic method called the afterimage algorithm is proposed. The proposed method was developed inspired by the fact that when we close our eyes after looking at a luminous image for a while, the vision still occurs in our minds. This is called an afterimage. The proposed method first pre-processes with the operator called afterimage and calculates the best and worst solution values. The visual angle value is then calculated, and new solutions are produced around this value. Three different datasets were used in experimental studies on data clustering. Accuracies of 96.66% for the iris plant dataset, 92% for the Wisconsin breast cancer dataset, and 95% for the occupancy detection dataset were obtained.