Dynamic chaotic Gold-Panning Optimizer and its typical engineering applications

Swarm intelligence algorithms are one of the key technologies in solving optimization problems for practical engineering applications, such as mechanical structure design, image analysis, process flow design, etc. Higher accuracy and efficiency mean better comprehensive performance in the practical engineering system based on optimization methods. Gold-Panning Algorithm is one of the swarm intelligence algorithms proposed in 2021, for solving image segmentation problems. However, the self decision-making mechanism based on multi-agent information interaction introduced in it weakens its convergence ability, resulting in its ability to exploit potential solutions being limited. Hence, chaotic maps were introduced to improve the optimization capacity and efficiency, which can provide a more reliable and effective ability to explore and exploit potential optimal solutions in different iteration stages. Moreover, a dynamic selection strategy is utilized to choose the better step-size iteration scheme between the Gaussian distribution and Levy flight. It can further strengthen the exploitation capability of the Gold-Panning Algorithm, reducing the possibility of premature convergence. Based on CEC'2020 benchmark functions, Dynamic Chaotic Gold-Panning Optimizer is compared with the other meta-heuristic algorithms to evaluate its performance and the results shows strong competitiveness in both robustness and accuracy for solving optimization problems. Then, based on the proposed optimizer, its binary variant is applied in feature selection. Besides, combined with the bilateral filtering and threshold segmentation model, the image blind denoising and image segmentation optimization schemes are proposed respectively. Simulation results indicate it presents an excellent comprehensive performance in solving the corresponding engineering task.(c) 2022 Elsevier B.V. All rights reserved.