Proceedings of the 10th annual conference companion on Genetic and evolutionary computation
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Multi-car elevator group supervisory control system using genetic network programming
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Constructing portfolio investment strategy based on time adapting genetic network programming
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Generalized time related sequential association rule mining and traffic prediction
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Genetic network programming with rule accumulation considering judgment order
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics
SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics
Evolving plural programs by genetic network programming with multi-start nodes
SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics
A model of portfolio optimization using time adapting genetic network programming
Computers and Operations Research
A method of association rule analysis for incomplete database using genetic network programming
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Genetic relation algorithm with guided mutation for the large-scale portfolio optimization
Expert Systems with Applications: An International Journal
Efficient program generation by evolving graph structures with multi-start nodes
Applied Soft Computing
Use of infeasible individuals in probabilistic model building genetic network programming
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Proceedings of the 13th annual conference on Genetic and evolutionary computation
An evolutionary approach to rank class association rules with feedback mechanism
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Assignment strategy selection for multi-car elevator group control using reinforcement learning
International Journal of Knowledge and Web Intelligence
Computers and Industrial Engineering
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
| Hi-index | 0.01 |
Elevator group supervisory control systems (EGSCSs) are designed so that the movement of several elevators in a building is controlled efficiently. The efficient control of EGSCSs using conventional control methods is very difficult due to its complexity, so it is becoming popular to introduce artificial intelligence (AI) technologies into EGSCSs in recent years. As a new approach, a graph-based evolutionary method named genetic network programming (GNP) has been applied to the EGSCSs, and its effectiveness is clarified. The GNP can introduce various a priori knowledge of the EGSCSs in its node functions easily, and can execute an efficient rule-based group supervisory control that is optimized in an evolutionary way. Meanwhile, double-deck elevator systems (DDESs) where two cages are connected in a shaft have been developed for the rising demand of more efficient transport of passengers in high-rise buildings. The DDESs have specific features due to the connection of cages and the need for comfortable riding; so its group supervisory control becomes more complex and requires more efficient group control systems than the conventional single-deck elevator systems (SDESs). In this paper, a new group supervisory control system for DDESs using GNP is proposed, and its optimization and performance evaluation are done through simulations. First, optimization of the GNP for DDSEs is executed. Second, the performance of the proposed method is evaluated by comparison with conventional methods, and the obtained control rules in GNP are studied. Finally, the reduction of space requirements compared with SDESs is confirmed.