Conference Program of WCICA 2012

Conference Program WCICA 2012
on the uncertain couplings.
◮ FrA10-5 14:50–15:10
State Estimation for a Class of Nonlinear Systems with Multi-package
Transmission, pp.2221–2226
Wang, Xiaofeng
He, Xiao
Wang, Zidong
Tsinghua Univ.
Tsinghua Univ.
Tsinghua Univ.
In this paper, the state estimation problem for a class of nonlinear systems
with multiple channels and correlated noises is studied in the
framework of Extended Kalman Filter (EKF). In networked systems,
when sensors are distributed in a large spatial area and multiple channels
are employed to transfer data from different sensors, measurements
may be lost at different rates. A diagonal matrix is utilized to
describe this phenomenon and an unbiased optimal nonlinear filter is
constructed in the least mean square sense. An illustrative example is
provided and the comparison of the results between our method and
the EKF shows the effectiveness of the proposed approach.
◮ FrA10-6 15:10–15:30
Kalman Filtering with Scheduled Measurements - Part I: Estimation
Framework, pp.2251–2256
You, Keyou
Xie, Lihua
Nanyang Technological Univ.
Nanyang Technological Univ.
This paper proposes an estimation framework under scheduled measurements
for linear discrete-time stochastic systems. Both controllable
and uncontrollable schedulers are considered. Under a controllable
scheduler, only the normalized measurement innovation greater than a
threshold will be communicated to the estimator. While under an uncontrollable
scheduler, the time duration between consecutive sensor communications
is triggered by an independent and identically distributed
process. For both types of scheduler, recursive estimators that achieve
the minimum mean square estimation error are derived, respectively.
Moreover, necessary and sufficient conditions for stability of the mean
square estimation error are provided.
FrA11 13:30–15:30 Room 311C
Invited Session: Intelligent Optimization and Evolutionary Computation
(I)
Chair: Chen, Jie
Co-Chair: Wang, Ling
Beijing Inst. of Tech.
Tsinghua Univ.
◮ FrA11-1 13:30–13:50
Online Route Planning for UAV Based on Model Predictive Control and
Particle Swarm Optimization Algorithm, pp.397–401
Peng, Zhihong
Li, Bo
Chen, Xiaotian
Wu, Jin Ping
Beijing Inst. of Tech.
Beijing Inst. of Tech.
Sci. & Tech. on Complex Land Sys. Simulation
Laboratory
Beijing Inst. of Tech.
Based on the model predictive control (MPC) and particle swarm optimization
(PSO) algorithm, an online three-dimension route planning
algorithm is proposed in this paper for UAV under the partially known
task environment with appearing threats. By using the preplanningonline
route tracking pattern, a reference route is planned in advance
according to the known environment information. During the flight, the
UAV tracks the reference route and detects the information of the environment
and threats. Based on the MPC and PSO algorithm, the online
route planning can be achieved by means of route prediction and receding
horizon optimization. In such a case, UAV can avoid the known and
appearing threats successfully. Compared to the traditional online route
planning algorithm, the proposed method, by making use of the partially
known information, can reduce the complexity, and meanwhile improve
the real-time and the feasibility of the planning route. Simulation results
demonstrate the effectiveness of the proposed algorithm.
◮ FrA11-2 13:50–14:10
Surrogate Models for User’s Evaluations base on Weighted Support
Vector Machine in IGAs, pp.144–149
Yang, Lei
China Univ. of Mining & Tech.
Gong, Dunwei
Sun, Xiaoyan
Sun, Jing
China Univ. of Mining & Tech.
China Univ. of Mining & Tech.
China Univ. of Mining & Tech.
Interactive genetic algorithms (IGAs) are effective methods of tackling
optimization problems involving qualitative indices by incorporating a
user’s evaluations into traditional genetic algorithms. The problem
of user fatigue resulting from the user’s evaluations, however, has a
negative influence on the performance of these algorithms. Substituting
the user’s evaluations with various surrogate models is beneficial
to alleviate user fatigue. Previous studies, however, have not taken full
advantage of information provided by samples obtained earlier when
constructing or updating these models. We focus on the issue of user
fatigue in this study, and present a novel method of effectively alleviating
user fatigue by substituting the user’s evaluations with a weighted
support vector machine (WSVM) and by incorporating it with the mechanism
of transfer learning. The proposed method is applied to the fashion
evolutionary design system and compared with previous effective
IGAs. The experimental results confirm the advantage of the proposed
method in both alleviating user fatigue and improving the precision of
the surrogate model.
◮ FrA11-3 14:10–14:30
A Hybrid Algorithm Based on Simplex Search and Differential Evolution
for Hybrid Flow-shop Scheduling , pp.643–648
Xu, Ye
Wang, Ling
Wang, Shengyao
Tsinghua Univ.
Tsinghua Univ.
Tsinghua Univ.
An effective hybrid algorithm by merging the searching mechanisms
of Nelder-Mead (NM) simplex method and differential evolution (DE) is
proposed to solve the hybrid flow-shop problem (HFSP) in this paper.
By using a special encoding, the NM and DE methods can be used
to solve permutation based combinatorial optimization problems. By
combining the DE based global search and NM method based local
search, the exploration and exploitation abilities are enhanced and well
balanced for solving the HFSP. Numerical testing results and comparisons
show that the proposed algorithm is effective, efficient and robust
in solving the HFSP.
◮ FrA11-4 14:30–14:50
A Compact Estimation of Distribution Algorithm for Solving Hybrid Flowshop
Scheduling Problem, pp.649–653
Wang, Shengyao
Wang, Ling
Xu, Ye
Tsinghua Univ.
Tsinghua Univ.
Tsinghua Univ.
According to the characteristics of the hybrid flow-shop scheduling
problem (HFSP), the permutation based encoding and decoding
schemes are designed and a probability model for describing the distribution
of the solution space is built to propose a compact estimation of
distribution algorithm (cEDA) in this paper. The algorithm uses only t-
wo individuals by sampling based on the probability model and updates
the parameters of the probability model with the selected individual.
The cEDA is efficient and easy to implement due to its low complexity
and comparatively few parameters. Simulation results based on some
widely-used instances and comparisons with some existing algorithms
demonstrate the effectiveness and efficiency of the proposed compact
estimation of distribution algorithm. The influence of the key parameter
on the performance is investigated as well.
◮ FrA11-5 14:50–15:10
A Memetic PSO based KNN Regression Method for Cycle Time Prediction
in a Wafer Fab, pp.474–478
Ni, Jiacheng
Tongji univ
In this paper, cycle time prediction of wafer lots is studied. A memetic
algorithm called GSMPSO by combining the PSO with a Gaussian
mutation operator and a Simulated Annealing (SA)-based local search
operator is developed to weight the features for K Nearest Neighbors
(KNN) regression. The GSMPSO-KNN regression method is adopted
to predict the cycle time of wafer lots. The experiment result demon-
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