Results 231 to 240 of about 644,441 (293)

Multi-objective optimization design of microchannel reactors for Fischer-Tropsch synthesis using CFD, GENN, and NSGA-II. [PDF]

RSC Adv
Ren S, Wang Y.
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Design of anti-frost smoke machine system for mountain orchard based on multi-objective optimization. [PDF]

Sci Rep
Lu Y, Zhang W, Lin Y, Cao Z, Wu H, Liu L, Song X, Lu Q.   +7 more
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Multi-objective optimization for dynamic logistics scheduling based on hierarchical deep reinforcement learning. [PDF]

Sci Rep
Su J, Dong S.
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Process Design of Vinyl-Coated Metal Sheet Stamping for Prevention of Delamination and Wrinkling by DNN-Based Multi-Objective Optimization. [PDF]

Materials (Basel)
Kim MG, Ryu JC, Lee CJ, Jang JS, Ko DC.
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Rank charged system search algorithm for optimization and operations research. [PDF]

Sci Rep
Rabiei MH, Eilbeigi E, Talatahari S, Alami M, Chen F, Gandomi AH.   +5 more
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Multi-objective enhanced interval optimization problem

Annals of Operations Research, 2020
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
P. Kumar, A. K. Bhurjee
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Sensitivity Analysis of Multi-objective Optimal Control Problems

Applied Mathematics & Optimization, 2021
Using the Mordukhovich subdifferential for the sensitivity analysis in some kinds of multi-objective parametric optimal control problems, one obtains new results on the corresponding efficient points.
Toan, N. T., Thuy, L. Q.
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Multi‐objective combinatorial optimization problems: A survey

Journal of Multi-Criteria Decision Analysis, 1994
AbstractIn the last 20 years many multi‐objective linear programming (MOLP) methods with continuous variables have been developed. However, in many real‐world applications discrete variables must be introduced. It is well known that MOLP problems with discrete variables can have special difficulties and so cannot be solved by simply combining discrete ...
Ulungu, E. L., Teghem, J.
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Hyper multi-objective evolutionary algorithm for multi-objective optimization problems

Soft Computing, 2016
Multi-objective optimization problems (MOPs) are very common in practice. To solve MOPs, many kinds of multi-objective evolutionary algorithms (MOEAs) are proposed. However, different MOEAs have different performances for different MOPs. Therefore, it is a time-consuming task to choose a suitable MOEA for a given problem.
Weian Guo, Ming Chen, Lei Wang, Qidi Wu
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