An improvement of continuous tabu search for global optimization

An improved continuous tabu search algorithm is developed for global optimization problems. In this tabu search algorithm, the aspiration level is introduced and the neighbor space of current solution is partitioned by a set of concentric hyperrectangles. The neighborhood of the current solution is generated by randomly selecting one point inside each concentric hyperrectangle and certain other points inside the central hyperrectangle. Experiments prove that the extra selection inside the central hyperrectangle can improve the performance of the tabu search algorithm.