A Benchmark of 25 Nonlinear Functions with Domain-Induced Discontinuity-Like for Global Optimization

A benchmark of 25 nonlinear optimization problems exhibiting domain-induced discontinuity-like behavior is proposed to support the performance evaluation of global optimization algorithms under feasibility-scarce and structurally disconnected landscapes. Referred to as the CPC Benchmark (Challenging Problems for Computation), the test suite consists of functions that are continuous on their natural domains, while infeasible regions and undefined evaluations are implicitly embedded in the objective, creating substantial challenges for global minimization. Six representative algorithms from diverse methodological paradigms are assessed to investigate the structural difficulty and discriminative capability of the proposed benchmark. Numerical results show that many functions possess extremely small feasible regions and strong precision sensitivity near feasibility boundaries, complicating initialization, feasibility discovery, and reliable objective evaluation. The findings demonstrate that the CPC benchmark provides a rigorous and discriminative testbed for advancing research on global optimization under hidden feasibility constraints.