Interactive Multi-Objective Decision-Support for the Optimization of Nonlinear Dynamic (Bio)Chemical Processes with Uncertainty

Vallerio, Mattia; Hufkens, Jan; Impe, Jan Van; Logist, Filip

doi:10.1016/b978-0-444-63578-5.50135-3

Cited by 1 publication

(2 citation statements)

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“…Regression modeling explores the relationships between several explanatory variables and one or more response variables [27,28]. 'Explanatory' variable is the independent variable, whereas, 'Response' variably depends on independent variables.…”

Section: Regression Modellingmentioning

confidence: 99%

“…These "non-inferior" or non-dominated solutions are referred to as pareto-optimal solutions and collectively represent the pareto set or front. The multi-objective evolutionary algorithms (MOEAs) populace based techniques for comprehending MOPs that have been created recently [25,27]. These algorithms manage the pursuit process around the global pareto-ideal district while keeping up sufficient populace differences to catch, however, many results on the pareto-front as could be allowed.…”

Section: Introductionmentioning

confidence: 99%

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Triple Objective Optimization of Cytotoxic Potency of Human Carcinoma Cell Lines of Ulva Fasciata Delile (A Marine Macro Alga) Using Non-Sorting Genetic Algorithm

Eswari

Ahmad

2016

Int J Pharm Pharm Sci

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Objective: The prime objective of our work was to develop an acceptable model for optimizing the cytotoxic potency of human carcinoma cell lines of marine microalga which gives true indications when compared to experimental results. Methods:The experimental result taken from Das et al., 2014 [12] for cytotoxic potency against human colon cancer (HT-29), human hepatocyte cancer (Hep-G2), and human breast cancer (MCF-7) cell lines was used to carry out a multi-objective (triple objective) optimization. Thirty nondominating solutions were considered for analyses of absorbance (y1), % cell survival (y2) and% cell inhibition (y3) data. The multi-objective (triple objective) optimization study by using experimental data of Das et al., 2014. Results:The solutions obtained by non-dominated sorting genetic algorithm (NSGA) have been compared with data obtained experimentally, and the results were found to be significant. This method has distinct advantages over other methods which relied heavily on statistical-regressionmodels, single objective optimization methods in the sense that it does triple-objective optimization. The results were significant when compared with experimental data corroborating acceptability of the proposed model. Conclusion:The solutions obtained by NSGA method, on comparison with experimental data, showed the applicability and suitability of the proposed model.

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Section: Regression Modellingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%