Bonobo Optimizer: A New Tool Toward the Global Optimization of Small Atomic Clusters

Area of Science:

• Computational Chemistry
• Materials Science
• Chemical Physics

Background:

• Determining global minimum energy configurations is crucial for understanding chemical systems.
• Increasing system size and complexity presents significant challenges in structural and bonding investigations.
• Metaheuristic algorithms offer potential solutions for complex optimization problems in chemistry.

Purpose of the Study:

• To introduce and evaluate the Bonobo optimizer (BO), a novel metaheuristic algorithm, for chemical structure and bonding investigations.
• To assess the performance, robustness, and efficiency of BO in finding global minimum energy configurations.
• To extend the application of BO to the optimization of atomic clusters.

Main Methods:

• Development and application of the Bonobo optimizer (BO), inspired by bonobo social and reproductive behaviors.
• Systematic evaluation of BO's performance on small carbon clusters.
• Parametric studies to assess algorithm adaptability and convergence.
• Statistical validation and comparative analysis against existing global optimization algorithms.

Main Results:

• The Bonobo optimizer (BO) consistently converges to global minimum structures for small carbon clusters.
• Parametric studies confirmed the algorithm's adaptability and robustness.
• Comparative analysis revealed BO's superior efficiency over established global optimization algorithms.
• Rigorous statistical validation supported the observed performance.

Conclusions:

• The Bonobo optimizer (BO) is a robust and efficient metaheuristic algorithm for solving complex chemical optimization problems.
• BO shows significant promise for the optimization of atomic clusters, advancing computational chemistry.
• This study validates BO as a valuable tool for elucidating global minimum energy configurations in chemical systems.