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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 pressure vessel design problem [1].
© Copyright Policy - open-access
Related In: Results  -  Collection


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fig2: Schematic of the pressure vessel design problem [1].

Mentions: The variables Ts and Th are discrete values which are integer multiples of 0.0625 inches. Figure 2 shows the cylindrical pressure vessel capped at both ends by hemispherical heads.


Conceptual comparison of population based metaheuristics for engineering problems.

Adekanmbi O, Green P - ScientificWorldJournal (2015)

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

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

fig2: Schematic of the pressure vessel design problem [1].
Mentions: The variables Ts and Th are discrete values which are integer multiples of 0.0625 inches. Figure 2 shows the cylindrical pressure vessel capped at both ends by hemispherical heads.

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.