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Model-free Optimal Control based Intelligent Cruise Control with Hardware-in-the-loop Demonstration
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| Pengarang | : | - |
| Nama Majalah/Jurnal | : | IEEE Computational intelligence |
| Volume / Edisi | : | 12 (No. 2) |
| Halaman | : | 56-69 |
| Abstrak | : | It is difficult to implement optimal control for a system whose model is unknown and operation environment is uncertain, such as the intelligent cruise control of vehicles. This article will address the problem from the perspective of reinforcement learning by learning the optimal policy from the state transition data. The model-free optimal control algorithm is employed to approximate the optimal control policy for the intelligent cruise control system, which considers the comfort performance and the safety performance comprehensively by setting up a total performance index. The algorithm is implemented by two multi-layer neural networks which are the critic network and the actor network. The critic and actor networks are employed to approximate the stateaction value function and the control action, respectively. In addition, a data collecting strategy is proposed to obtain the state transition data distributed uniformly in the state action space from the running trajectory of the host car. The critic network and the action network are trained alternatively by the collected data until converging. The convergent action network is used to obtain the optimal control policy. At last, the policy is tested on a hardware-in-the-loop simulator built upon dSPACE by comparing with a linear quadratic regulator (LQR) controller and a proportion integration differentiation (PID) controller. Results show its excellent performance on both aspects of the safety and the comfort. |
Differential Evolution Based on Self-Adaptive Fitness Function for Automated Test Case Generation
| Pengarang | : | Fangqing Liu,Xiaoyan Zhuo,Han Huang,Zhifeng Hao |
| Nama Majalah/Jurnal | : | IEEE Computational intelligence |
| Volume / Edisi | : | 12 (No. 2) |
| Halaman | : | 46-55 |
| Abstrak | : | One of the challenges of search-based software testing is the automated test case generation for path coverage (ATCG-PC). There have been several meta-heuristic algorithms proposed to cope with this challenge. However, the cost of test cases generated by the algorithms is still high and even infinite for complete path coverage. The goal of the current study is to propose an improved meta-heuristic algorithm for ATCGPC. Unlike the previous research, our study focuses on how to use more heuristic information to generate more uncovered paths with fewer test cases. We combine a self-adaptive fitness function and a modified differential evolution algorithm to implement this plan. Follow-up experimental studies on eight benchmark problems of classical programs show that the proposed algorithm outperforms those solutions obtained by immune genetic algorithm, artificial bee colony, and random search. |
PENYERBUAN RRC KE VIETNAM: SUATU PELAJARAN?
| Pengarang | : | - |
| Nama Majalah/Jurnal | : | Analisa |
| Volume / Edisi | : | X-4, APRIL (No. 4) |
| Halaman | : | 337-361 |
| Abstrak | : | - |
HUBUNGAN INDONESIA-RRC
| Pengarang | : | - |
| Nama Majalah/Jurnal | : | Analisa |
| Volume / Edisi | : | X-4, APRIL (No. 4) |
| Halaman | : | 323-336 |
| Abstrak | : | - |
Hyper-Heuristic Based Product Selection for Software Product Line Testing
| Pengarang | : | - |
| Nama Majalah/Jurnal | : | IEEE Computational intelligence |
| Volume / Edisi | : | 12 (No. 2) |
| Halaman | : | 34-45 |
| Abstrak | : | The Feature Model (FM) is a fundamental artifact of the Software Product Line (SPL) engineering. It represents all SPL variabilities and commonalities and is generally used to derive products for SPL testing. However, the testing of all products is almost impossible in practice. Therefore, search-based approaches can be found in the literature to select the most interesting ones. Among them, the approaches that use Multi-Objective Evolutionary Algorithms (MOEAs) are very promising since this selection problem is impacted by many factors. Yet for the tester it is not always easy to choose the best algorithm, and to configure parameters and operators to solve the problem. To help in this task, we introduce in this paper a Hyper-Heuristic (HH) approach. HHs are methodologies used to select or generate heuristics. Our HH approach dynamically selects the best operators, considering four objectives of the problem: the number of products, pairwise coverage, mutation score, and dissimilarity of products. The approach is implemented and evaluated with four MOEAs: NSGA-II, SPEA2, IBEA, and MOEA/D-DRA, and two selection methods: random and Upper Confidence Bound (UCB) based. Evaluation results show that the HH-based NSGA-II algorithm generates the best results. Moreover, the UCB method outperforms the random selection on large instances |
POLA PEMILIKAN, ORGANISASI EKONOMI, DAN PENANAMAN MODAL ASING DI RRC
| Pengarang | : | - |
| Nama Majalah/Jurnal | : | Analisa |
| Volume / Edisi | : | X-4, APRIL (No. 4) |
| Halaman | : | 310-322 |
| Abstrak | : | - |
STRUKTUR KEKUASAAN POLITIK DI RRC DALAM ERA POST-MAO
| Pengarang | : | - |
| Nama Majalah/Jurnal | : | Analisa |
| Volume / Edisi | : | X-4, APRIL (No. 4) |
| Halaman | : | 296-309 |
| Abstrak | : | - |
Multi-Level Random Walk for Software Test Suite Reduction
| Pengarang | : | Zongzheng Chi,Jifeng Xuan,Zhilei Ren,Xiaoyuan Xie,He Guo |
| Nama Majalah/Jurnal | : | IEEE Computational intelligence |
| Volume / Edisi | : | 12 (No. 2) |
| Halaman | : | 24-33 |
| Abstrak | : | Which test cases should be selected to save the time of software testing? Due to the large time cost of running all test cases, it is necessary to run representative test cases to shorten the software development cycle. Test suite reduction, an NP-hard problem in software engineering, aims to select a subset of test cases to reduce the time cost of test execution in satisfying test requirements. Recently, search based software engineering provides a new direction to test suite reduction by connecting software engineering problems with computational intelligence methods. In this paper, we propose a multi-level optimization algorithm to simplify the original problem instance of test suite reduction. In each level, we search for local optimal solutions with random walk in potential subsets of the test suite. The problem scale is reduced by locking the intersection of local optima and by discarding shielded test cases with no con­tribution to test requirements. We compare our algorithm with state-of-the-art methods on test suites of ten large-scale open source projects. Experiments show that our algorithm can more efficiently find optima on five out of six projects, in which Integer Linear Programming (ILP) can find optima; for the other four projects that ILP fails to solve, our algorithm provides the best solutions among heuristics in comparison. |
PERGANTIAN KEPEMIMPINAN DI RRC
| Pengarang | : | Budi S. Satari |
| Nama Majalah/Jurnal | : | Analisa |
| Volume / Edisi | : | X-4, APRIL (No. 4) |
| Halaman | : | 287-295 |
| Abstrak | : | - |
CIS Publication Spotlight
| Pengarang | : | Haibo He,Garibaldi, Jon,Kay Chen Tan,Kendall, Graham,Yaochu Jin,Yew Soon Ong |
| Nama Majalah/Jurnal | : | IEEE Computational intelligence |
| Volume / Edisi | : | 12 (No. 2) |
| Halaman | : | 20-22 |
| Abstrak | : | - |