Atsuya Fujino

An elevator group control system with floor attribute control method and system optimization using genetic algorithms

A new concept is proposed for an elevator group control system which can change control settings according to individual floor utilization situations. The floor attribute based control method uses a combination of floor attribute based evaluation and car attribute based evaluation. Computer simulations show it can improve multiple preferential floor simulations at the same time. Additionally, an on-line parameter tuning method using genetic algorithms is proposed. The floor attribute control method needs some dozens of control parameters to be tuned according to each buildings traffic, and the proposed method can do it. The elitest preserving strategy is an effective way to keep control stability. A diversity recovering operation, which initializes population, excluding the elitest individual when traffic flow data have changed, improves the following ability for transitions of utilized conditions.

A parameter tuning method using genetic algorithms for an elevator group control system

This paper describes a new parameter tuning method for an elevator group control system. In an elevator group control system, tuning parameters describing elevator use and the building environment are important. This new tuning method uses improved genetic algorithms to follow environment changes and to give robustness. Keeping diversity is important in following environment changes. The paper also describes a method of adding random chromosomes and a method of adding other evaluations chromosomes to keep diversity. Their effects are shown by simulations.

A parameter tuning method for an elevator group control system using a genetic algorithm

This paper describes a new parameter tuning method for an elevator group control system. In such a system, tuning parameters describing elevator use and the building environment are important. The new tuning method uses improved genetic algorithms to follow environment changes and to give robustness. Keeping diversity is important in following environment changes. The paper also describes the addition of the random chromosome method and addition of other estimation methods to maintain diversity. Their effects are shown by simulations.

An online tuning method for multiobjective control of elevator group

The authors describe an online tuning method of control parameters for a multiobjective elevator group control system. To adapt a control parameter to each building, a conventional system tunes the control parameter by using a combined method of a learning function and a built-in simulator. However, an increase of tuning time, caused by the addition of control parameters, can become a serious problem. The proposed method can restrict research points by using macro knowledge of the system domain. Then, tuning time and tuning accuracy are well-balanced. Computer simulations show quick settling and good tuning results. Field data obtained with the system verify the findings.<<ETX>>