Golden sine cosine SALP swarm algorithm for shape matching using atomic potential function

Abstract

AbstractSalp swarm algorithm (SSA) is one of the efficient recent meta‐heuristic optimization algorithms, where it has been successfully utilized in a wide range of optimization problems in different fields. In the research process, it is found that it is very difficult to maintain the balance between the exploration and exploitation capabilities of a certain algorithm. Therefore, one of the main purposes of this article is to provide an algorithm that can intelligently balance between exploration and exploitation, so that it can balance exploration and exploitation capabilities. Later, in the research process, it was found that the sine and cosine function and the salp foraging trajectory have a high mathematical similarity, which greatly improves the optimization ability of the algorithm. In addition, the variable neighbourhood strategy can appropriately expand the optimization range of the algorithm. So in this paper, a novel golden sine cosine salp swarm algorithm with variable neighbourhood search scheme (GSCSSA‐VNS) is proposed, the another objective of proposing this algorithm is as a new optimization method for shape matching. As a relatively new branch, atomic potential matching (APM) model is inspired by potential field attractions. Compared to the conventional edge potential function (EPF) model, APM has been verified to be less sensitive to intricate backgrounds in the test image and far more cost effective in the computation process. Experimental results of four realistic examples show that GSCSSA‐VNS is able to provide very competitive results and outperforms the other algorithms.