بهبود الگوریتم فرا ابتکاری پافین قطبی مبتنی بر نگاشت آشوب

چکیده مقاله :

الگوریتم پافین قطبی که در سال ۲۰۲۴ معرفی شده، با الهام از رفتار شکار این پرنده در محیط‌های هوایی و دریایی طراحی شده و از مفاهیم هوش جمعی برای مدل‌سازی حرکت گروهی پافین‌ها استفاده می‌کند. این الگوریتم با بهره‌گیری از استراتژی‌های مختلف حرکتی، تلاش می‌کند تعادل مناسبی میان فرآیندهای اکتشاف و بهره‌برداری در فضای جستجو ایجاد کند. با این حال، وابستگی برخی از مراحل جستجوی این الگوریتم به فرآیندهای تصادفی می‌تواند موجب ایجاد تغییرات نامنظم در مسیر حرکت عامل‌ها، کاهش کنترل تنوع جمعیت و افزایش احتمال همگرایی زودهنگام شود.در این پژوهش، یک نسخه بهبودیافته از الگوریتم پافین قطبی با عنوان الگوریتم پافین قطبی آشوبی ارائه شده است که در آن از نگاشت آشوبی لجستیک به‌منظور بهبود کیفیت تولید مقادیر هدایت‌کننده جستجو و ارتقای رفتار حرکتی عامل‌ها استفاده می‌شود. سازوکار پیشنهادی با بهره‌گیری از ویژگی‌هایی نظیر غیرخطی بودن و پوشش مناسب فضای جستجو، تلاش می‌کند تعادل بهتری میان اکتشاف و بهره‌برداری ایجاد کند.عملکرد الگوریتم پیشنهادی بر روی ۱۲ تابع معیار در ابعاد مختلف ارزیابی شده و با الگوریتم پایه پافین قطبی و چندین الگوریتم فراابتکاری مطرح شامل گرگ خاکستری، بهینه‌سازی نهنگ، ازدحام سالپ‌ها و نسخه‌های آشوبی آن‌ها مقایسه شده است. نتایج تجربی نشان می‌دهد که الگوریتم پافین قطبی آشوبی در مقایسه با الگوریتم پایه پافین قطبی موجب بهبود کیفیت پاسخ‌ها، افزایش پایداری جستجو و رفتار همگرایی مناسب‌تر شده و در مقایسه با سایر روش‌های مرجع نیز عملکرد رقابتی ارائه می‌دهد. همچنین نتایج آزمون‌های آماری ویلکاکسون و فریدمن نشان داد که تفاوت عملکرد الگوریتم پیشنهادی نسبت به روش پایه الگوریتم پافین قطبی از نظر آماری معنادار است.

چکیده انگلیسی :

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. 

منابع و مأخذ:

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