Sergeyev, Y. D., Kvasov, D. E., & Mukhametzhanov, M. S. (2018). On the efficiency of nature-inspired metaheuristics in expensive global optimization with limited budget. Scientific Reports, 8:453.
Sergeyev, Y. D., & Kvasov, D. E. (2017). Lipschitz Global Optimization. In SpringerBriefs in Optimization (pp. 1–17). Springer New York.
Kvasov, D. E., & Sergeyev, Y. D. (2015). Deterministic approaches for solving practical black-box global optimization problems. Advances in Engineering Software, 80, 58–66.
Floudas, C. A., & Gounaris, C. E. (2008). A review of recent advances in global optimization. Journal of Global Optimization, 45(1), 3–38.
Floudas, C. A., Akrotirianakis, I. G., Caratzoulas, S., Meyer, C. A., & Kallrath, J. (2005). Global optimization in the 21st century: Advances and challenges. Computers & Chemical Engineering, 29(6), 1185–1202.
Pardalos, P. M., Romeijn, H. E., & Tuy, H. (2000). Recent developments and trends in global optimization. Journal of Computational and Applied Mathematics, 124(1–2), 209–228.
Floudas, C., A., & Pardalos, P., M. (1996). State of the art in global optimization. Kluwer Academic Pub.
Pardalos, P. M. (1994). On the passage from local to global in optimization. In “Mathematical Programming: State of the Art” (Edited by J.R. Birge & K.G. Murty), The University of Michigan, 220–247.