Revolution Optimization Algorithm: A New Human-based Metaheuristic Algorithm for Solving Optimization Problems

This paper presents a new metaheuristic algorithm called the Revolution Optimization Algorithm (ROA) to solve complex optimization problems.Inspired by societal revolutions, ROA models its process in three phases: (i) revolution ideology, where the foundation for change is laid, (ii) revolutionary movement, representing transformation, and (iii) increasing self-awareness, reflecting adaptation and improvement.Each phase contributes to a robust mathematical structure that guides the algorithm's operations.The efficiency of ROA has been tested on 23 benchmark functions, covering unimodal, high-dimensional multimodal, and fixed-dimensional multimodal problems.ROA demonstrates strong exploration and exploitation capabilities, achieving a well-balanced search process.Comparative evaluations with ten well-known metaheuristic algorithms reveal that ROA provides superior solutions in most cases, showcasing its competitive edge in global optimization.The algorithm efficiently handles diverse challenges, offering reliable and high-quality solutions where other algorithms may fall short.With its unique structure and exceptional performance across both theoretical benchmarks and real-world scenarios, ROA emerges as a powerful and versatile tool for solving a broad range of optimization problems.