Haibing Gao’s research while affiliated with Huazhong University of Science and Technology and other places

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Publications (4)


Pattern Classification and Prediction of Water Quality by Neural Network with Particle Swarm Optimization
  • Conference Paper

January 2006

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175 Reads

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25 Citations

Chi Zhou

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Haibing Gao

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Chuanyong Peng

Water pollution has posed a severe problem in modern society. Evaluation of water quality is a meaningful topic today. To identify the specific water category and predict the water quality in the future, a particle swarm optimization (PSO) based artificial neural network (ANN) approach is presented. The data investigated from the Yangtze River are chosen as the original cases to construct the ANN model and testify both the classification and prediction ability of this method. Compared with other classical methods, the proposed one can obtain high quality and efficiency without losing computational expense. Experimental results show PSO is a robust training algorithm and could be extended to other real world pattern classification and prediction applications


Particle swarm optimization based algorithm for constrained layout optimization

January 2005

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51 Reads

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37 Citations

Kongzhi yu Juece/Control and Decision

Layout optimization is an NP-hard problem. It also belongs to complex nonlinear constrained optimization problem. In view of this problem, a new methodology based on particle swarm optimization (PSO) is developed to optimize layout parameters. A constraint handling strategy suit for PSO is proposed. Furthermore, improvement is made by using direct search to intensify local search ability of PSO algorithm. Simulation results show that the proposed algorithm improves the quality of the solution while lowering the computational cost.


Particle swarm optimization based algorithm for machining parameter optimization

July 2004

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34 Reads

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15 Citations

Selection of machining parameters is an important step in process planning. In view of this problem, a new methodology based on particle swarm optimization (PSO) is developed to optimize machining conditions. First, by introducing the concept of history constraint satisfaction, constraint handling strategy suit for PSO optimization mechanism is presented. Furthermore, improvement is made by using direct search to intensify the local search ability of PSO algorithm. In addition, mathematical model for milling operation is established with respect to maximum production rate, subject to a set of practical machining constraints. The simulation results show that compared with genetic algorithm and simulated annealing, the proposed algorithm can improve the quality of the solution while speeding up the convergence process.


A Generic Constraint Handling for Particle Swarm Optimization

28 Reads

Motivated by the memory mechanism of particle swarm optimization (PSO), we discuss the issue of Fair Updating in constrained optimization by PSO. Based on that, we investigate the possibility of extending constraint handling methods of evolutionary algorithms to the use by PSO. After that, we propose a generic constraint handling approach, which observes the Fair Updating. This approach transfers the task of constraint handing to the issue of updating elitist memory particles under constraint environment. In this approach, a generalized Pareto-based dominance, which considers information of objective function and constraint violation, is used to update elitist memory particles. Furthermore, under the proposed constraint handling, we investigate the effects of PSO model on constrained optimization, which is studied from the aspects of information flow structure and stochastic exploration. The results show: (1) PSO with the proposed constraint handling generates promising results in terms of convergence to feasibility and global convergence in the feasible region; (2) PSO model has a direct impact on the performance of constrained optimization.

Citations (3)


... This problem was originally introduced by Teng et al. in 1994 [2], and it is recognized as a strongly Nondeterministic Polynomial time hard (NP-hard) problem. Over the years, various algorithms have been proposed to tackle the LOP, including those based on Particle Swarm Optimization (PSO) [3], Wang-Landau sampling method [4], and Genetic Algorithm (GA) [5][6]. ...

Reference:

MULTI-OBJECTIVE GENETIC ALGORITHM BASED METHOD FOR SATELLITE PAYLOADS CONFIGURATION DESIGN OPTIMIZATION
Particle swarm optimization based algorithm for constrained layout optimization
  • Citing Article
  • January 2005

Kongzhi yu Juece/Control and Decision

... Zare Abyaneh's (2014) study focused on highlighting the efficacy of the ANN model in estimating BOD and COD variables. The implementation of PSO in training the network, as demonstrated by Zhou et al. (2006), led to considerable advancements in the accuracy and effectiveness of ANN models for prediction purposes. These 39, 0.96, 0.38, 0.70, 0.69, 0.23, 0.26, and 0.72, respectively). ...

Pattern Classification and Prediction of Water Quality by Neural Network with Particle Swarm Optimization
  • Citing Conference Paper
  • January 2006

... These particles are randomly initialized and fly through multidimensional space. During the flying, these particles update its velocity and position based on the experience of its own and the whole population [7,8]. Optimal PID control of a brushless DC motor using PSO and BF techniques ...

Particle swarm optimization based algorithm for machining parameter optimization
  • Citing Conference Paper
  • July 2004