Liang Ximing

A Trust Regional Algorithm for Bound Constrained Minimization

A trust region algorithm is proposed for box constrained nonlinear optimization. At each step of the algorithm a quadratic model problem in a box is minimized. Global convergence and quadratic convergence rate to a strong local minimizer are given. Computational results are presented to show the efficiency of the algorithm

Active set truncated-newton algorithm for simultaneous optimization of distillation column

An active set truncated-Newton algorithm (ASTNA) is proposed to solve the large-scale bound constrained sub-problems. The global convergence of the algorithm is obtained and two groups of numerical experiments are made for the various large-scale problems of varying size. The comparison results between ASTNA and the subspace limited memory quasi-Newton algorithm and between the modified augmented Lagrange multiplier methods combined with ASTNA and the modified barrier function method show the stability and effectiveness of ASTNA for simultaneous optimization of distillation column.

A new hybrid algorithm based on collaborative line search and Particle Swarm Optimization

Recently Particle Swarm Optimization (PSO) algorithm gained popularity and employed in many engineering applications because of its simplicity and efficiency. The performance of the PSO algorithm can further be improved by using hybrid techniques. There are various hybrid PSO algorithms published in the literature where researchers combine the benefits of PSO with other heuristics algorithms. In this paper, we propose a cooperative line search particle swarm optimization (CLS-PSO) algorithm by integrating local line search technique and basic PSO (B-PSO). The performance of the proposed hybrid algorithm, examined through four typically nonlinear optimization problems, is reported. Our experimental results show that CLS-PSO outperforms basic PSO.

A new one-step-ahead predictive control algorithm

A new one-step-ahead predictive control algorithm for a family of nonlinear MISO systems is presented. First, least squares support vector machine (LSSVM) with the quadratic multinomial function kernel is used to obtain the off-line nonlinear systemspsila model. Then, the analytical equation which includes the unknown optimal input increment and the known inputs/outputs is derived according to the predictive control mechanism. And finally the analytical equation is solved through numerical algorithm. The simulation result show the effectiveness and advantage of the one-step-ahead predictive control algorithm.

Improved Genetic Algorithms to Solving Constrained Optimization Problems

The slow convergence speed and the lack of effective constraint handling strategies are the major concerns when applying genetic algorithms (Gas) to constrained optimization problem. An improved genetic algorithm was proposed by dividing population into three parts: optimal subpopulation, elitists subpopulation and spare subpopulation. We applied genetic algorithm on three subpopulations with different evolutionary strategies. Isolation of optimal subpopulation was to improve convergence speed. Population diversity was kept by spare subpopulation setting and aperiodically decreasing of the size of optimal subpopulation. Gene segregation was carried out by crossover operation between optimal subpopulation and spare subpopulation. Combination of penalty function method and the strategy of elitists preservation by setting elitists subpopulation was used to constraint handling. Some numerical tests have been made and the results show that the algorithm is effective.

Using co-line search technique to improve performance of PSO

Recently, variants of hybrid PSO algorithms are presented, but hybrid Optimization technique combining classical local line search with PSO is less focused on. In this paper, we proposed a new hybrid cooperative evolutionary algorithm by integrating local line search technique and basic PSO. The performance of our algorithm tested using four typical nonlinear optimization problems is reported and compared with that of basic PSO. Results show that performance of CLS-PSO algorithm is much better than basic PSO, and has considerable steady and robust.

A State Space Model to Infer Interwell Connectivity Only Form Injection and Production Data in Waterfloods

Production and injection rates are the most abundant data available in any injection-production project. One can analyze these data to obtain valuable and useful information about interwell connectivity. The oil reservoir is considered to be an input-output system with the injection rates as the input and production rates as the output. A state space model is developed to infer the interwell connectivity only from the injection and production data on a reservoir. A synthetic field with five injectors and four producers is used to test the model. The simulation results show that the long time dependent behavior between injectors and producers can be satisfactorily captured by the proposed state space model.

DISO fuzzy control algorithm with potentially-inherited variable universe and its convergence property

In this paper, a novel fuzzy control algorithm is presented for conventional DISO(double-input & single-output) fuzzy controller. The new algorithm is based on potentially-inherited variable universe. By defining an auxiliary fuzzy singleton set and a fuzzy singleton transition function, the operation of variable universe is greatly simplified at the consequent part of inference. So, the algorithms computation is significantly reduced, comparing with the method of directly-inherited variable universe or the method of integral variable universe. Finally, the convergence property of this control algorithm is proved theoretically.

Truth-flowing fuzzy control algorithm and its application in temperature control system

A kind of product-truth-flowing fuzzy control algorithm is presented in this paper. Firstly, the steps of the algorithm are listed in detail. The algorithm holds advantage that fuzzy reasoning process is intuitionistic and its computation is simple. Secondly, based on the algorithm, a constant-temperature control system is designed with MAX7651 MCU, Pt100, MOC3041. Finally, practical control result shows that the performance of control system is excellent, its rise-time is about 21 minutes, the overshot of temperature is only 0.4 degC, and real-time temperature waves between -0.3degC and +0.3degC at expectation value.

Sequential Bound Constrained Minimization Technique for Large-scale Process System Optimization

Based on sequential unconstrained programming method, the sequential bound constrained programming algorithms for large-scale process system optimization are studied in this paper. Since mild variables are introduced according to all inequality constraints, the penalty function of our algorithms only constraints the penalty terms for equality constraints. A series of bound constrained sub-problems instead of a series of unconstrained sub-problems are solved in these algorithms. The sequential bound constrained programming algorithms are performed in two stages. The inner stage is the bound constrained minimization of the argumented Lagrange penalty function in which a new set of primal variables is found. The outer stage is performed to update the Lagrange multipliers and penalty parameters, check for convergence and accordingly reinitiate another bound constrained minimization or declare convergence. Further more, a modified truncated-Newton algorithm is proposed to solve the bound constrained sub-problems. Finally, numerical experiments are made for two kinds of alterable dimension nonlinear programming problems, which proves the stability and effectiveness of the algorithms for large-scale process system optimization.