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Tomlab Optimization tomlab optimization toolbox
Tomlab Optimization Toolbox, supplied by Tomlab Optimization, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/product/toolbox+tomlab/tomlab+optimization+toolbox/pm29457696-110-10-13
Average 90 stars, based on 1 article reviews
tomlab optimization toolbox - by Bioz Stars, 2026-09
90/100 stars

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Article Title: A review on optimization of composite structures Part I: Laminated composites
Article Snippet: Correspondent Author, E-mail: Shakeri@aut.ac.ir

Article Title: Reshaping a multimode laser beam into a constructed Gaussian beam for generating a thin light sheet.
Article Snippet: A cc ep te d A rti cl e the TOMLAB optimization toolbox (TOMLAB software AB, Sweden) to find matching values for the integer parameters p, q, p1, and q1 and the fmincon solver provided by the MATLAB optimization toolbox (MathWorks, Germany) as a sub-solver to find best parameter values for the model parameters a, b, c, d, and w01, w02, w03, w04.

Article Title: Integrating Flux Balance Analysis into Kinetic Models to Decipher the Dynamic Metabolism of Shewanella oneidensis MR-1
Article Snippet: The internal optimization was a typical quadratic programming (QP) problem and was solved using the CPLEX solver in the TOMLAB optimization toolbox (TOMLAB Optimization Inc, Seattle, WA) within MATLAB (R2009a).

Article Title: Design of dimensionally stable composites using efficient global optimization method
Article Snippet: TOMLAB optimization toolbox is used to solve the model problems.

Article Title: Adaptive importance sampling for optimization under uncertainty problems
Article Snippet: Design-under-uncertainty problems where a probabilistic performance is adopted as objective function and its estimation is obtained through stochastic simulation are discussed.. The focus is on reducing the computational burden associated with the stochastic simulation through adaptive implementation of importance sampling (IS) across the iterations of the optimization algorithm.. The proposed formulation relies only on available information (i.e., function evaluations) from the current iteration of the optimization process to improve estimation accuracy in subsequent iterations, and therefore corresponds to a IS selection with a small additional computational burden.

Article Title: Global Optimization for Integrated Design and Control of Computationally Expensive Process Models
Article Snippet: Included in the Tomlab toolbox,21 it solves costly global optimization problems using a radial basis functions (RBF) interpolation algorithm.5,6 It fits a response surface (based on splines) to data collected by evaluating the objective function at some points and then applies a global optimization algorithm (a DIRECT22 implementation called glcFast from the same toolbox) and a local algorithm (npsol23) using different initial points over that surrogate model.

Article Title: Optimal spatial evaluation of a pro rata vaccine distribution rule for COVID-19
Article Snippet: The solution was found using TOMLAB (v. 8.4) (72, 73) and the accompanying PROPT toolbox (74).

Article Title: Divide and Conquer Optimization for Closed Loop Supply Chains
Article Snippet: Environmental concerns and government regulations have encouraged/forced real-world supply chains to take back used product for recycle.. A smart supply chain should therefore leverage significantly on the returned “used products” to produce new products at nominal cost and time.. In such closed loop supply chains, the crucial challenge is to synchronize the used products recycling system, the production facility and the new product distribution system in the presence of uncertain customer demands and used product returns.



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