Global Optimization Methods based on Tabu Search

Many activities can be formulated as optimization problems. Because of the computational complexity of these problems, exact optimization techniques of operations research like linear programming or dynamic programming are for the most part unfeasible for large-scale problems. Therefore, researchers, engineers, and scientists have used metaheuristic search techniques to find near optimal, good-enough optimal or even the global solutions. This work encompasses three parts. The first part provides a concise review of the most prominent metaheuristic concepts currently available and gives essential preliminaries together with definition of the combinatorial optimization problems. In the second part the new nonlinear global optimization routines based on the TS strategy are described: the Gradient Tabu Search (GTS), the Gradient Only Tabu Search (GOTS), and the Tabu Search with Powell’s Algorithm (TSPA). The new algorithms are compared with other approaches using various well-known test functions with varying dimensionality. In the last part of the work the GOTS is applied for global minimum energy conformation.