This paper presents an approach to synthesizing optimization test functions that couples generative adversarial networks and adaptive neuro-fuzzy systems. A generative adversarial network produces optimization landsca...
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This paper presents an approach to synthesizing optimization test functions that couples generative adversarial networks and adaptive neuro-fuzzy systems. A generative adversarial network produces optimization landscapes from a database of known optimization test functions, and an adaptive neuro-fuzzy system performs regression on the generated landscapes to provide closed-form expressions. These expressions can be implemented as fuzzy basis function expansions. Eight databases of two-dimensional optimization landscapes reported in the literature are used to train the generative network. Exploratory landscape analysis over the generated samples reveals that the network can lead to new optimization landscapes with features of interest. In addition, fuzzy basis function expansions provide the best approximation results when compared against two symbolic regression frameworks over several selected landscapes. Examples are used to illustrate the ability of these functions to model complex surface artifacts such as plateaus. The proposed approach can be used as a mathematical collaboration tool that couples generative artificial and computational intelligence techniques to formulate high-dimensional optimizationtest problems from two-dimensional synthesized functions.
This research work presents the development of a modified bat algorithm (mBA) using elite opposition - based learning. The bat algorithm (BA), which is a nature inspired meta-heuristic algorithm, works on the basis of...
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ISBN:
(纸本)9781509064229
This research work presents the development of a modified bat algorithm (mBA) using elite opposition - based learning. The bat algorithm (BA), which is a nature inspired meta-heuristic algorithm, works on the basis of the echolocation behavior of bat. It, however, has a poor exploration capability leading to it easily getting stuck in local optima. The mBA is developed by modifying the BA with elite opposition - based learning (EOBL) in order to diversify the solution search space and the inertial weight in order to improve its exploitation capability. The performance of the proposed mBA was compared with that of the standard BA using seven benchmark optimization test functions. The simulation results showed that the mBA is superior to the standard BA by obtaining global optimal result of most of the testfunctions. All simulations were carried out using MATLAB R2013b.
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