Physically Informed Population Initialization Improves Bonobo Optimizer Searches for Atomic Cluster Global Minima

Reliable global optimization of atomic clusters is limited not only by the ruggedness of the potential energy surface but also by the quality of the starting population supplied to the search algorithm. We examine this issue for the Bonobo Optimizer (BO) using a workflow that combines external PyAR-style population initialization, local Lennard-Jones relaxation, geometry repair, and structural similarity filtering. The initializer generates physically reasonable and structurally diverse trial clusters before the BO loop begins, while the relaxation and filtering steps map candidates to local minima and reduce redundant population updates. On Lennard-Jones clusters, the unmodified BO framework becomes unreliable beyond LJ₁₃, and local relaxation with similarity filtering alone remains insufficient for the double-funnel LJ₃₈ benchmark, reaching the global minimum only six times in 25 runs. Adding the Tabu-style initializer changes this outcome: LJ₃₈ is solved in all 20 independent runs, and the full LJ₂-LJ₁₀₀ benchmark reaches 943 successes in 1030 runs, corresponding to an overall success rate of 91.5%. The main remaining failures occur for LJ₇₅-LJ₇₇, consistent with the known difficulty of the Marks-decahedral exceptions. These results identify physically informed population construction as a practical route for improving BO-based cluster global optimization.