Improvement of the Arctic Puffin Metaheuristic Algorithm Based on a Chaotic Map
Subject Areas : ICT
Hamid Reza Ghasemi
1
,
Ali Akbar Neghabi
2
*
,
Babak Lotfi
3
1 - Department of Computer Engineering and Information Technology, Sab.C., Islamic Azad University, Sabzevar, Iran
2 - Department of Computer Engineering and Information Technology, Sab.C., Islamic Azad University, Sabzevar, Iran
3 - PhD student, Department of Computer Engineering and Information Technology, Sab.C., Islamic Azad University, Sabzevar, Iran
Keywords: Arctic Puffin Optimization (APO), Chaotic Optimization, Logistic Map, Metaheuristic Algorithm, Evolutionary Computation.,
Abstract :
The Arctic Puffin Optimization (APO) algorithm, introduced in 2024, is inspired by the hunting behavior of puffins in aerial and marine environments and utilizes swarm intelligence concepts to model the collective movements of puffins. By employing various movement strategies, this algorithm aims to establish an appropriate balance between exploration and exploitation processes in the search space. However, the dependence of some search stages on random processes may lead to irregular variations in the movement trajectories of search agents, reduced control over population diversity, and an increased risk of premature convergence. In this study, an improved version of the Arctic Puffin Optimization algorithm, referred to as the Chaotic Arctic Puffin Algorithm (CAPA), is proposed. The proposed approach incorporates a Logistic chaotic map to enhance the generation quality of search-guiding parameters and improve the movement behavior of optimization agents. By exploiting the nonlinear properties and effective space-filling capability of chaotic sequences, the proposed mechanism aims to achieve a more effective balance between exploration and exploitation. The performance of the proposed algorithm is evaluated on 12 benchmark functions with different dimensions and compared with the original Arctic Puffin Optimization algorithm as well as several well-established metaheuristic algorithms, including Grey Wolf Optimizer (GWO), Whale Optimization Algorithm (WOA), Salp Swarm Algorithm (SSA), and their chaotic variants. The experimental results demonstrate that the Chaotic Arctic Puffin Algorithm improves solution quality, search stability, and convergence behavior compared with the original APO algorithm, while maintaining competitive performance against other reference methods. Furthermore, the results of the Wilcoxon signed-rank test and Friedman test confirm that the performance difference between the proposed algorithm and the original Arctic Puffin Optimization algorithm is statistically significant.
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