Journal Article10.1016/J.ASOC.2006.12.005
A simple self-adaptive Differential Evolution algorithm with application on the ALSTOM gasifier
Amin Nobakhti,Hong Wang +1 more
- 01 Jan 2008
- Vol. 8, Iss: 1, pp 350-370
117
TL;DR: This sensitivity to its control parameters is studied here and a simple randomised self-adaptive scheme is proposed for the DE mutation weighting factor F.
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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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Citations
Differential Evolution: A Survey of the State-of-the-Art
TL;DR: A detailed review of the basic concepts of DE and a survey of its major variants, its application to multiobjective, constrained, large scale, and uncertain optimization problems, and the theoretical studies conducted on DE so far are presented.
Seeker Optimization Algorithm for Optimal Reactive Power Dispatch
TL;DR: In this work, a seeker optimization algorithm (SOA)-based reactive power dispatch method is proposed, based on the concept of simulating the act of human searching, which is superior to the other listed algorithms and can be efficiently used for optimal reactivePower dispatch.
485
DE/BBO: a hybrid differential evolution with biogeography-based optimization for global numerical optimization
Wenyin Gong,Zhihua Cai,Charles X. Ling +2 more
- 01 Apr 2010
TL;DR: Compared with other state-of-the-art DE approaches, DE/BBO performs better, or at least comparably, in terms of the quality of the final solutions and the convergence rate.
367
Seeker Optimization Algorithm for Digital IIR Filter Design
TL;DR: A seeker-optimization-algorithm (SOA)-based evolutionary method is proposed for digital IIR filter design and results show that SOA is superior or comparable to the other algorithms for the employed examples and can be efficiently used for IIRfilter design.
234
Constraint-Handling in Evolutionary Optimization
Efrn Mezura-Montes
- 15 Apr 2009
TL;DR: This book is the result of a successful special session on constraint-handling techniques used in evolutionary algorithms within the Congress on Evolutionary Computation in 2007, with the aim of putting together recent studies on constrained numerical optimization using evolutionary algorithms and other bio-inspired approaches.
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