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The cost function is multimodal since we consider the composite layup design problem.
We present a new version of the Merlin optimization package that contains an interface routine enabling the use of Merlin as a non-interactive local optimizer, and a capability to search for the global minimum when the objective function is multimodal.
This function is multimodal.
The problem is even more difficult if the function is multimodal.
A function is multimodal if it has two or more local optima.
For multistable stimuli, the likelihood function is multimodal, reflecting the different possible interpretations (Sundareswara and Schrater 2008).
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Although optimum estimation performance can be attained, it requires sufficiently precise initial estimates for global convergence because the corresponding cost functions are multimodal.
In the worst case, the cost function is largely multimodal and needs an efficient identification technique to reach an acceptable solution within a reasonable duration.
We anticipate that the objective function for hyperparameter optimization is multimodal, and many local optima that achieve similar results exist.
In the case of the arterial pressure waveform modelling, the function to be optimized is multimodal, containing multiple local optima.
Evolutionary algorithms (EAs) are a class of general optimization algorithms which are applicable to functions that are multimodal, non-differentiable, or even discontinuous.
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