Deterministic Multi-step Crossover Fusion: A Handy Crossover Composition for GAs
PPSN VII Proceedings of the 7th International Conference on Parallel Problem Solving from Nature
Multi-Objective Optimization for Aerodynamic Designs by Using ARMOGAs
HPCASIA '04 Proceedings of the High Performance Computing and Grid in Asia Pacific Region, Seventh International Conference
Optimal elevator group control by evolution strategies
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartII
Design and implementation of a fuzzy elevator group control system
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
MceSim: A Multi-Car Elevator Simulator
IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences
IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences
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Multi-Car Elevator (MCE) that has several elevator cars in a single shaft attracts attention for improvement of transportation in high-rise buildings. However, because of lack of experience of such novel systems, design of controller for MCE is very difficult engineering problem. One of the promising approaches is application of evolutionary optimization to from-scratch optimization of the controller through discrete event simulation of the MCE system. In the present paper, the authors propose application of evolutionary multi-objective optimization to design of traffic-sensitive MCE controller. The controller for MCE is optimized for different traffic conditions in multi-objective way. By combining the multi-objective optimization with the exemplar-based policy (EBP) representation that has adequate flexibility and generalization ability as a controller, we can successfully design a controller that performs well both in the different traffic conditions and works adequately by generalization in the conditions not used in the optimization process.