مشخصات پژوهش

Background and Objectives: The development of effective meta-heuristic algorithms is crucial for solving complex optimization problems. This paper introduces the Society Deciling Process (SDP), a novel socio-inspired meta-heuristic algorithm that simulates the social categorization into deciles based on metrics such as income, occupation, and education. The objective of this research is to introduce the SDP algorithm and evaluate its performance in terms of convergence speed and hit rate, comparing it with seven well-established meta-heuristic algorithms to highlight its potential in optimization tasks. Methods: The SDP algorithm's efficacy was evaluated using a comprehensive set of 14 general test functions, including benchmarks from the CEC 2019 and CEC 2022 competitions. The performance of SDP was compared against seven established meta-heuristic algorithms: Artificial Hummingbird Algorithm (AHA), Dwarf Mongoose Optimization algorithm (DMO), Reptile Search Algorithm (RSA), Snake Optimizer (SO), Prairie Dog Optimization (PDO), Fick’s Law Optimization (FLA), and Gazelle Optimization Algorithm (GOA). Statistical analysis was conducted using Friedman's rank and Wilcoxon signed-rank tests to assess the relative performance in terms of exploration, exploitation capabilities, and proximity to the optimum solution. Results: The results demonstrated that the SDP algorithm outperforms its counterparts in terms of convergence speed and hit rate across the selected test functions. In statistical tests, SDP showed significantly better performance in exploration and exploitation, leading to a higher proximity to the optimum solution compared to the other algorithms. Furthermore, when applied to five complex engineering design problems, the SDP algorithm exhibited superior performance, outmatching the state-of-the-art algorithms in terms of effectiveness and efficiency. Conclusion: The Society Deciling Process (SDP) algorithm introduces a novel and effective approach to optimiz