Engineering Optimization: Methods and Applications

By A. Ravindran, K. Ragsdell, G. Reklaitis. 2006

876 citations recorded by the source corpus.

Subfields: Industrial and Manufacturing Engineering

View Paper: https://www.semanticscholar.org/paper/e68624fbe7993cef5aff43faecd5cc70aafa0417

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In Industrial and Manufacturing Engineering

Citations in the corpus

A new meta-heuristic algorithm for continuous engineering optimization: harmony search theory and practice (2005, methodology): “16, is a practical design problem that has been often used as a benchmark for testing different optimization methods [36,44–47].”
Innovization: innovating design principles through optimization (2006, background): “The welded beam design problem is well studied in the context of single-objective optimization [14].”
Cuckoo search algorithm: a metaheuristic approach to solve structural optimization problems (2011, methodology): “For this problem, Ravindran et al. [ 27 ] reported the minimum value of 6.84241 using a random search method.”
Optimization of segmented constrained layer damping with mathematical programming using strain energy analysis and modal data (2010, background): “G. Lepoittevin, G. Kress / Materials and Design 31 (2010) 14–24 19 polyhedron of N+1 in N dimensions [ 25 ].”
Introducing Robustness in Multi-Objective Optimization (2006, result): “This section presents a case study of a robust optimization of a welded beam problem, the single-objective version of which has been commonly used in classical and evolutionary optimization studies ( Reklaitis et al., 1983; Deb, 1991).,The welded beam design problem is a four-variable constrained o”
Prairie Dog Optimization Algorithm (2022, neutral): “Subject to þ 2 : 2 8 : 94 range: 0 (cid:4) x 1 ; x 2 ; x 3 (cid:4) 100 ; 0 (cid:4) x 4 (cid:4) 5 [80].”
Two-dimensional optimization of material composition of functionally graded materials using meshless analyses and a genetic algorithm (2006, methodology): “The most prevalent is the penalty function method [49].”
Reference point based multi-objective optimization using evolutionary algorithms (2006, background): “The stress and buckling terms are non-linear to design variables and are given as follows [20]:”
Untangling of 2D meshes in ALE simulations (2004, methodology): “Therefore, the corners of the feasible set polygon can be found by minimizing different linear functions along with the appropriate inequality constraints using the simplex method [17,18].”