Zhao Yanwei

A fuzzy matter-element scheme multi-objective optimization method based on genetic algorithm

This paper presents a fuzzy matter-element optimization method for a machine scheme based on a genetic algorithm. For example, for designing a drive scheme, at first, the fuzzy matter-element is applied to build a model for the drive scheme. Then, the adaptive macroevolution genetic algorithm is applied to resolve this model during which the real number coding method is used to code chromosomes for the drive scheme. During the process of resolving the model, the rhombic thought method has been combined with the genetic algorithm organically. The paper discusses the key technology of the genetic algorithm for designing the drive scheme in detail. At last, this method is applied to design a drive scheme and a satisfying result is obtained, so the method is verified to be valid by this example.

A heuristic for optimal job scheduling problem with a common due window on parallel and identical machines

The paper is concerned with the job scheduling problem with a common due window. After giving an optimal algorithm for the single machine problem, a mathematical model for the similar problem on parallel and identical machines is presented. Because the job scheduling problem on parallel and identical machines, which may be NP-complete, is much more complex than that on a single machine, a heuristic algorithm is given to find an approximate solution after two important theorems are proved. The numerical example illustrates that the heuristic algorithm is very effective in obtaining a near-optimal solution.

Modified DMC method and its realization based on BP neural networks

Presents a modified dynamic matrix control (DMC) algorithm and its realization based on BP neural networks with higher nonlinear mapping and learning ability, predictive precision is increased and the control quality is improved by this control algorithm. In addition, the predictive control algorithm is extended to nonlinear systems. The results of simulation show that this algorithm is simple and applicable; its robustness is high; the transient and steady-state quality and the oscillation of the control variable are improved.