A simple self-adaptive Differential Evolution algorithm with application on the ALSTOM gasifier |
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| Affiliation: | 1. College of Information Science and Technology, Beijing University of Chemical Technology, 100029, China;2. Control Systems Centre, The University of Manchester, Manchester M60 1QD, United Kingdom;1. School of Information and Software Engineering, University of Electronic Science and Technology of China, China;2. Department of Computer and Information Science, Linköping University, Sweden;3. Department of Computer Science, University of Texas at Dallas, USA;4. Department of Computer Science and Engineering, University of Notre Dame, USA;1. School of Systems Engineering, University of Reading, Reading RG6 6AY, UK;2. Electronics and Computer Science, University of Southampton, Southampton SO17 1BJ, UK;3. King Abdulaziz University, Jeddah 21589, Saudi Arabia;1. State Key Laboratory of Synthetical Automation for Process Industries, Northeastern University, Shenyang 110819, China;2. Control Systems Centre, The University of Manchester, PO Box 88, Manchester M60 1QD, UK |
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| Abstract: | Differential Evolution (DE) has gathered a reputation for being a powerful yet simple global optimiser with continually outperforming many of the already existing stochastic and direct search global optimisation techniques. It is however well established that DE is particularly sensitive to its control parameters, most notably the mutation weighting factor F. This sensitivity is further studied here and a simple randomised self-adaptive scheme is proposed for the DE mutation weighting factor F. The performance of this algorithm is studied with the use of several benchmark problems and applied to a difficult control systems design case study. |
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