Luo, W., Schoning, C., Li, L. and Li, Y. (2016) Heuristic Search Towards the Invention of an Optimal-Ignition Internal Combustion Engine. In: CEC 2016: IEEE World Congress on Computational Intelligence, Vancouver, Canada, 24-29 July 2016, pp. 4634-4641. ISBN 9781509006236 (doi: 10.1109/CEC.2016.7744381)
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118966.pdf - Accepted Version 1MB |
Abstract
Most internal combustion engines are built on compression or spark ignition, which is far from optimal and the problem of which is more than optimization. This paper first improves a genetic algorithm (GA) for such an application, aiming at the potential invention of a homogeneous charge microwave ignition (HCMI) engine. For an HCMI system, search for optimal emitters under the intrinsic constraints of resonant frequencies forms a coupled constraint optimization problem and poses an intractable challenge to the GA and virtual prototyping for the invention. A predefined GA (PGA) is then developed to handle appropriate frequency ranges for this problem so as to allow the parameters of the emitter, as well as its structure, to be optimized in an evolutionary process. The heuristic search is compared with the deterministic NM simplex and the nondeterministic conventional GA. Results show that while the NM and GA heuristics find an insufficient mode, the PGA often finds the global maximum, with a higher convergence rate and independent of the algorithm's initial settings. When the complexity of the problem increases with the number of variables, the PGA also delivers a robust performance while the NM and the GA yield divergent results. This application confirms the viability and power of evolutionary heuristics in inventing novel real-world solutions if properly adapted.
| Item Type: | Conference Proceedings |
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| Status: | Published |
| Refereed: | Yes |
| Glasgow Author(s) Enlighten ID: | Luo, Miss Wuqiao and Li, Professor Yun |
| Authors: | Luo, W., Schoning, C., Li, L., and Li, Y. |
| College/School: | College of Science and Engineering > School of Engineering College of Science and Engineering > School of Engineering > Systems Power and Energy |
| ISBN: | 9781509006236 |
| First Published: | First published in 2016 IEEE Congress on Evolutionary Computation (CEC): 4634-4641 |
| Publisher Policy: | Reproduced in accordance with the publisher copyright policy |
| Related URLs: |
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