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Here, it should be noted that we problem (Eq. 4) in two steps whereby first maximizing biomass objective and then maximizing/minimizing targeted worst-case scenario with minimum biomass constrained at the value obtained in previous step to make sure that there is no other alternative optima present.
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Most of the currently available optimization algorithms, specially local deterministic methods, may lead to suboptimal solutions if multiple local optima are present, as shown in [ 2, 3].
Since the large majority of bacteria present growth optima at or near neutral pH values, most laboratory-based biodegradation studies have been carried out in this pH range.
The results of the comparative mycelium linear growth rate examination covering a wide temperature range (12 47°C) showed that most strains presented their optima at a narrow value area of 27 32°C (Table 2).
The purpose of this paper is to review some recent results that demonstrate the importance of identifying global optima of MINLP formulations.We present two global optimization 'success stories' based on the application of global optimization techniques to two design problems.
However, finding the global optimum for multimodal problems, i.e. those presenting multiple local optima, requires robust and efficient global optimization methods.
However the NLP arising from the application of the control vector parameterization method are frequently multimodal (i.e. presenting multiple local optima), due to the highly nonlinear nature of the dynamics [ 41].
Despite the coherence between temperature optima, the pH optimum in the present study (pH 6.4) slightly deviates from that data.
Thirteen OPTIMA patients were excluded from the present analysis on the basis of missing phosphorus and/or PTH values, leaving a total of 539 patients (n = 357 cinacalcet; n = 182 conventional care).
As an additional benefit, the full mapping of the objective function allows one to identify multiple optima easily, an important feature not presented by conventional optimization methods.
In the local search mechanism, a simulated annealing local search algorithm based on Nowicki and Smutnicki's neighborhood is presented to exploit local optima.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com