Multipath Adaptive Tabu Search for a Vehicle Control Problem

Abstract

Tabu search has become acceptable worldwide as one of the most efficient intelligent searches applied to various real-world problems. There have been different modifications made to the generic tabu search in recent years to achieve better performances. Among those reviewed in the introduction of this paper, the adaptive tabu search ATS has incorporated the backtracking and the adaptive search radius mechanisms that help accelerate the search and release it from a local solution lock. The paper explains an enhancement made to the ATS to accomplish multipath ATS MATS algorithms. Performances of the ATS and the MATS are evaluated using surface optimization problems, and results are presented in the paper. Finally, the MATS is applied to solve a real-world vehicle control problem. 2Journal of Applied Mathematics the conventionally simple TS cannot completely handle the deadlock problems. With this challenging task, some researchers have proposed modified versions of the original TS to find alternative, more potential versions to overcome such problems. Distinctively modified version of the TS include the reactive tabu search RTS 9, 10 , the parallel tabu search PTS 11-13 , the modified tabu search MoTS 14-16 , the probabilistic tabu search PrTS 17-19 , and the hybrid tabu search HTS 20-29 .Recently, the adaptive tabu search ATS has been launched 30 as a modified versions of the original TS. The ATS consists of the backtracking BT and the adaptive search radius AR mechanisms. As firstly introduced in 1996 7 , the BT regarded as a diversification strategy is used for the search to escape from the deadlocks caused by local minima. The AR concept introduced in 1989 31 and regarded as an intensification strategy is applied to speed up the search process. The convergence analysis and the performance evaluation of the ATS were reported 30 . In addition, the ATS has been successfully applied to various engineering problems, for examples, electrical system protection 32 , dynamical system identification 33 , control synthesis 34 , and acoustic signal processing 35 .This article proposes the multipath adaptive tabu search MATS and begins with the review of the ATS algorithms in Section 2. Section 3 explains the MATS in details and presents some algorithm analyses. Performance evaluations of the MATS compared with the ATS via surface optimization problems can be found in Section 4. An application of the proposed MATS to the control synthesis problem of a scaled vehicle the Illinois Roadway Simulator system IRS 36 is illustrated in Section 5. The application is considered as a special case n 2 of the n-dimensional continuous optimization problems. Conclusion follows in Section 6.

The Energy Saving for Separately Excited DC Motor Drive via Model Based Method

“…The validation results show that the K st and K h values are appropriate to use at any operating points. Therefore, the equation to calculate the total power losses of the motor using in the paper is shown in (9). In the paper, the voltage drop across the brushes (U e ) is set to 2 V [1].…”

“…In order to perform its effectiveness, the ATS has been tested against several well-known benchmark functions, that is, Bohachevsky, Rastrigin, Shekel's foxholds, Shubert and Schwefel functions [9][10][11][12][13]. Moreover, the convergence property of the ATS has been proved to assure that it can reach the optimal solution within finite search time [9][10][11][12][13][14].…”

Instantaneous Power Theory with Fourier and Optimal Predictive Controller Design for Shunt Active Power Filter

“…The adaptive Tabu search or ATS method [34][35][36][37][38][39][40][41][42][43][44][45][46][47] is used to design the predictive current controller to minimize %THD ,av of source currents after compensation. The review of the ATS algorithm is described in this section.…”

“…The conventional predictive current control uses the first-order Lagrange equation to approximate the predicted reference currents. Presently, it is well known that there are many artificial intelligence (AI) techniques to apply for the optimization problems in the engineering researches such as the multiobjective harmony search (MOHS) [29], artificial bee colony (ABC) [30,31], competition particle swarm optimization (CPSO) [32], genetic algorithm (GA) [33], and adaptive Tabu search (ATS) [34][35][36][37][38][39][40][41][42][43][44][45][46][47]. The ATS method is developed by Puangdownreong et al in 2002 [34].…”

“…In order to perform its effectiveness, the ATS has tested against several wellknown benchmark functions, that is, Bohachevsky, Rastrigin, Shekel's foxholds, Shubert, and Schwefel functions [42][43][44][45][46]. Moreover, the convergence property of the ATS has been proved to assure that it can reach the optimal solution within finite search time [42][43][44][45][46][47]. Thus, the ATS is selected to design the predictive current controller in the paper.…”