Painting Training Based Optimization: A New Human-based Metaheuristic Algorithm for Solving Engineering Optimization Problems

This study introduces a completely different perspective on optimization through the development of a novel human-based metaheuristic algorithm named Painting Training Based Optimization (PTBO). Inspired by the intricate and iterative human activities observed during painting training, PTBO models these creative and systematic processes to effectively address optimization challenges. The algorithm's foundation is rooted in the concepts of exploration and exploitation, which are essential for achieving a balance between searching the solution space widely and refining promising areas. The theoretical framework of PTBO is comprehensively described, followed by detailed mathematical modeling of its two-phase operation. To evaluate its capability, the algorithm is tested on 22 constrained optimization problems sourced from the well-regarded CEC 2011 test suite. The experimental results show that PTBO excels at producing competitive and high-quality solutions. A comparative analysis with 12 other well-known metaheuristic algorithms underscores PTBO's superior performance, particularly in handling complex benchmark functions. The results show that the proposed PTBO approach outperformed competing algorithms in all (22) optimization problems of the CEC 2011 test suite. The findings highlight PTBO's effectiveness in solving real-world optimization problems, showcasing its potential to outperform existing methods. By offering a completely different optimization approach, PTBO contributes a significant and innovative tool to address challenges in engineering and other applied domains.