Test-case generator for nonlinear continuous parameter optimization techniques
The experimental results reported in many papers suggest that making an appropriate a priori choice of an evolutionary method for a nonlinear parameter optimization problem remains an open question. It seems that the most promising approach at this stage of research is experimental, involving the design of a scalable test suite of constrained optimization problems, in which many features could be tuned easily. It would then be possible to evaluate the merits and drawbacks of the available methods, as well as to test new methods efficiently. In this paper, we propose such a test-case generator for constrained parameter optimization techniques. This generator is capable of creating various test problems with different characteristics including: 1) problems with different relative sizes of the feasible region in the search space; 2) problems with different numbers and types of constraints; 3) problems with convex or nonconvex evaluation functions, possibly with multiple optima; and 4) problems with highly nonconvex constraints consisting of (possibly) disjoint regions. Such a test-case generator is very useful for analyzing and comparing different constraint-handling techniques.
test-case generator constrained optimization evolutionary computation nonlinear programming