Divine Religions Algorithm: a novel social-inspired metaheuristic algorithm for engineering and continuous optimization problems

Ali Toufanzadeh Mozhdehi, Nima Khodadadi, Mohaddeseh Aboutalebi, El-Sayed M. El-Kenawy, Abdelazim G. Hussien, Weiguo Zhao, Mohammad H. Nadimi-Shahraki, Seyedali Mirjalili

Abstract: Classical optimization methods struggle with complex, NP-hard real-world problems. To overcome these challenges, researchers have explored approximate methods inspired by natural behavioral patterns. One prominent branch of these metaheuristic methods draws from human behaviors observed in politics, sports, and social interactions. This study introduces a novel optimization technique, the Divine Religions Algorithm (DRA), which employs an evolutionary socio-economic approach inspired by religious societies. The algorithm models interactions among followers, missionaries, and leaders, organizing followers into religious and political schools. Agents such as promotion, miracles, substitution, and reward-penalty mechanisms, along with an elitism-based search, enhance follower beliefs. The DRA’s performance is benchmarked against seven popular metaheuristic techniques: Sine-Cosine Algorithm, Tunicate Swarm Algorithm, Moth-flame Optimization, Gray Wolf Optimization, Whale Optimization Algorithm, Fire Hawk Optimization, and Smell Agent Optimization. We evaluate the DRA using twenty-three standard benchmarks, considering key indicators such as accuracy, convergence, efficiency, and cost. Additionally, five real-world optimization problems are employed to demonstrate the DRA’s superiority in handling constrained engineering problems.The results indicate that the DRA significantly outperforms other methods, delivering superior outcomes across multiple aspects, proving its efficacy in solving complex optimization problems. The source codes of the DRA algorithm are publicly available at https://nimakhodadadi.com/algorithms-%2B-codes.