A Python/C library for bound-constrained global optimization with continuous GRASP

作者：Silva R M A; Resende M G C^*; Pardalos P M; Hirsch M J

来源：Optimization Letters, 2013, 7(5): 967-984.

DOI：10.1007/s11590-012-0475-7

• GRASP
• Continuous GRASP
• Global optimization
• Multimodal functions
• Continuous optimization
• Heuristic
• Stochastic algorithm
• Stochastic local search
• Nonlinear programming

This paper describes , a GNU-style dynamic shared Python/C library of the continuous greedy randomized adaptive search procedure (C-GRASP) for bound constrained global optimization. C-GRASP is an extension of the GRASP metaheuristic (Feo and Resende, 1989) and has been used to solve unstable and nondifferentiable problems, as well as hard global optimization problems, such as chemical equilibrium systems and robot kinematics applications (Hirsch et al. in Optim lett 1:201-212, 2007). After a brief introduction to C-GRASP, we show how to download, install, configure, and use the library through an illustrative example.

• 出版日期2013-6