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Conceptual comparison of population based metaheuristics for engineering problems.

Adekanmbi O, Green P - ScientificWorldJournal (2015)

Bottom Line: Several extensions of differential evolution have been adopted in solving constrained and nonconstrained multiobjective optimization problems, but in this study, the third version of generalized differential evolution (GDE) is used for solving practical engineering problems.GDE3 metaheuristic modifies the selection process of the basic differential evolution and extends DE/rand/1/bin strategy in solving practical applications.The performance of the metaheuristic is investigated through engineering design optimization problems and the results are reported.

View Article: PubMed Central - PubMed

Affiliation: Department of Finance and Information Management, Durban University of Technology, P.O. Box 101112, Scottsville, Pietermaritzburg 3209, South Africa.

ABSTRACT
Metaheuristic algorithms are well-known optimization tools which have been employed for solving a wide range of optimization problems. Several extensions of differential evolution have been adopted in solving constrained and nonconstrained multiobjective optimization problems, but in this study, the third version of generalized differential evolution (GDE) is used for solving practical engineering problems. GDE3 metaheuristic modifies the selection process of the basic differential evolution and extends DE/rand/1/bin strategy in solving practical applications. The performance of the metaheuristic is investigated through engineering design optimization problems and the results are reported. The comparison of the numerical results with those of other metaheuristic techniques demonstrates the promising performance of the algorithm as a robust optimization tool for practical purposes.

No MeSH data available.


Schematic of the welded beam design problem [1].
© Copyright Policy - open-access
Related In: Results  -  Collection


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fig1: Schematic of the welded beam design problem [1].

Mentions: The welded beam problem is designed to minimize the fabrication cost by subjecting it to some constraints such as bending stress (σ), shear stress (τ), end deflection (δ), and buckling load (Pc). The design variables of the optimization problem are the thickness of the beam (b), the thickness of the weld (h), the welded joint length (l), and the beam width (t). Figure 1 shows the welded beam design structure.


Conceptual comparison of population based metaheuristics for engineering problems.

Adekanmbi O, Green P - ScientificWorldJournal (2015)

Schematic of the welded beam design problem [1].
© Copyright Policy - open-access
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC4383342&req=5

fig1: Schematic of the welded beam design problem [1].
Mentions: The welded beam problem is designed to minimize the fabrication cost by subjecting it to some constraints such as bending stress (σ), shear stress (τ), end deflection (δ), and buckling load (Pc). The design variables of the optimization problem are the thickness of the beam (b), the thickness of the weld (h), the welded joint length (l), and the beam width (t). Figure 1 shows the welded beam design structure.

Bottom Line: Several extensions of differential evolution have been adopted in solving constrained and nonconstrained multiobjective optimization problems, but in this study, the third version of generalized differential evolution (GDE) is used for solving practical engineering problems.GDE3 metaheuristic modifies the selection process of the basic differential evolution and extends DE/rand/1/bin strategy in solving practical applications.The performance of the metaheuristic is investigated through engineering design optimization problems and the results are reported.

View Article: PubMed Central - PubMed

Affiliation: Department of Finance and Information Management, Durban University of Technology, P.O. Box 101112, Scottsville, Pietermaritzburg 3209, South Africa.

ABSTRACT
Metaheuristic algorithms are well-known optimization tools which have been employed for solving a wide range of optimization problems. Several extensions of differential evolution have been adopted in solving constrained and nonconstrained multiobjective optimization problems, but in this study, the third version of generalized differential evolution (GDE) is used for solving practical engineering problems. GDE3 metaheuristic modifies the selection process of the basic differential evolution and extends DE/rand/1/bin strategy in solving practical applications. The performance of the metaheuristic is investigated through engineering design optimization problems and the results are reported. The comparison of the numerical results with those of other metaheuristic techniques demonstrates the promising performance of the algorithm as a robust optimization tool for practical purposes.

No MeSH data available.