A Study On Improvement Of Continuous Tabu Search And Its Applications

Two improved continuous tabu search algorithms, ITS and TSSQP, are developed for global optimization problems. Their applications on system identification and training of recurrent neural network are also studied. The main results can be summarized as follows:(1) An improved continuous tabu search algorithm, ITS, is developed. In the ITS, the neighbor space of current solution is partitioned by a set of concentric hyperrectangles. Inside each concentric hyperrectangle, one point is randomly selected as a neighbor of the current solution. Considering the intensification strategy an improvement is made by selecting a certen number of points also inside the central hyperrectangle following an aspiration criterion. All selected points generate the neighborhood of the current solution. Numerical simulation results prove that the extra selection inside the central hyperrectangle enables the ITS to obtain more precise global miminum with less time cost.(2) Further improvement of the tabu search is proposed by combine with the SQP method. As a kind of random search algorithm, the performance of TS is limited. The SQP, good at fast convergencey in local search, is introduced to propose a new algorithm, TSSQP. In TS_SQP, the SQP algorithm starts from each point in the neighborhood of current solution and converges a local miminum; All these local minima consititute the new neighborhood of the current solution; then the program continues by exacting ITS. Compared with ITS, the simulation results shuw that TS_SQP can obtain more precise global miminum and costs less time.(3) The improved tabu search algorithms ITS and TS_SQP are applied on system identification. The problems of system identification are transformed to optimization problems in parameter space, and then the ITS and TS_SQP algorithms are used to obtain the global optimal estimation of the system parameters. The results of simulations on a tank model, a discrete and a continuous two-order system, and a highorder system demonstrate the feasibility and effectiveness of ITS and TS_SQP.(4) The improved tabu search algorithms, ITS and TS_SQP, are applied on the training of recurrent neural network. The training problem is cast as optimization problem in parameter space. The numerical simulation results show that they are simple yet effective.