Journal
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
Volume 252, Issue 3, Pages 701-727Publisher
ELSEVIER
DOI: 10.1016/j.ejor.2015.12.018
Keywords
MINLP; Deterministic global optimization; Derivative-free; Grey-/Black-box; Constraints
Funding
- National Science Foundation [CBET-0827907, CBET-1263165]
- Royal Academy of Engineering Fellowship
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This manuscript reviews recent advances in deterministic global optimization for Mixed-Integer Nonlinear Programming (MINLP), as well as Constrained Derivative-Free Optimization (CDFO). This work provides a comprehensive and detailed literature review in terms of significant theoretical contributions, algorithmic developments, software implementations and applications for both MINLP and CDFO. Both research areas have experienced rapid growth, with a common aim to solve a wide range of real-world problems. We show their individual prerequisites, formulations and applicability, but also point out possible points of interaction in problems which contain hybrid characteristics. Finally, an inclusive and complete test suite is provided for both MINLP and CDFO algorithms, which is useful for future benchmarking. (C) 2015 Elsevier B.V. All rights reserved.
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