A new constraint test-case generator and the importance of hybrid optimizers

• Published in: European journal of operational research Vol. 173; no. 2; pp. 419 - 443
• Main Authors: Shang, Yi, Fromherz, Markus P.J., Crawford, Lara
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier B.V 01.09.2006; Elsevier; Elsevier Sequoia S.A
• Series: European Journal of Operational Research
• Subjects: Applied sciences; Constrained optimization problems (COPs); Constraint satisfaction problems (CSPs); Exact sciences and technology; Hybrid optimizers; Methods; Nonlinear test problem generator; Operational research. Management science; Operations research; Optimization; Optimization techniques; Performance analysis; Phase transitions; Problem solving; Quadratic programming; Studies; Hybrid optimizers; Constrained optimization problems (COPs); Performance analysis; Nonlinear test problem generator; Constraint satisfaction problems (CSPs); Sequential test; Individual method; Redundancy; Empirical method; Constraint satisfaction; Quadratic programming; Phase transitions; Penalty method; Manipulator; Constrained optimization; Penalty function; Sequential method; Augmented Lagrangian; Test method; Lagrangian function; Lagrangian method
• ISSN: 0377-2217; 1872-6860
• DOI: 10.1016/j.ejor.2005.02.073

In the past decade, significant progress has been made in understanding problem complexity of discrete constraint problems. In contrast, little similar work has been done for constraint problems in the continuous domain. In this paper, we study the complexity of typical methods for non-linear constraint problems and present hybrid solvers with improved performance. To facilitate the empirical study, we propose a new test-case generator for generating non-linear constraint satisfaction problems (CSPs) and constrained optimization problems (COPs). The optimization methods tested include a sequential quadratic programming (SQP) method, a penalty method with a fixed penalty function, a penalty method with a sequence of penalty functions, and an augmented Lagrangian method. For hybrid solvers, we focus on the form that combines two or more optimization methods in sequence. In the experiments, we apply these methods to solve a series of continuous constraint problems with increasing constraint-to-variable ratios. The test problems include artificial benchmark problems from the test-case generator and problems derived from controlling a hyper-redundant modular manipulator. We obtain novel results on complexity phase transition phenomena of the various methods. Specifically, for constraint satisfaction problems, the SQP method is the best on weakly constrained problems, whereas the augmented Lagrangian method is the best on highly constrained ones. Although the static penalty method performs poorly by itself, by combining it with the SQP method, we show a hybrid solver that is significantly better than any of the individual methods on problems with moderate to large constraint-to-variable ratios. For constrained optimization problems, the hybrid solver obtains much better solutions than SQP, while spending comparable amount of time. In addition, the hybrid solver is flexible and can achieve good results on time-bounded applications by setting parameters according to the time limits.