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A self-adaptive global best harmony search algorithm for continuous optimization problems

Pan, Quan-Ke ; Suganthan, P.N. ; Tasgetiren, M. Fatih ; Liang, J.J.

Applied mathematics and computation, 2010-04, Vol.216 (3), p.830-848 [Periódico revisado por pares]

Amsterdam: Elsevier Inc

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Título:
A self-adaptive global best harmony search algorithm for continuous optimization problems
Autor: Pan, Quan-Ke ; Suganthan, P.N. ; Tasgetiren, M. Fatih ; Liang, J.J.
Assuntos: Algorithms ; Benchmarking ; Calculus of variations and optimal control ; Combinatorics ; Combinatorics. Ordered structures ; Computation ; Continuous optimization ; Evolutionary algorithms ; Exact sciences and technology ; Exploration ; Global analysis, analysis on manifolds ; Graph theory ; Harmony search ; Learning ; Mathematical analysis ; Mathematical models ; Mathematics ; Meta-heuristics ; Numerical analysis ; Numerical analysis. Scientific computation ; Numerical methods in mathematical programming, optimization and calculus of variations ; Numerical methods in optimization and calculus of variations ; Optimization ; Sciences and techniques of general use ; Searching ; Topology. Manifolds and cell complexes. Global analysis and analysis on manifolds
É parte de: Applied mathematics and computation, 2010-04, Vol.216 (3), p.830-848
Notas: ObjectType-Article-2
SourceType-Scholarly Journals-1
ObjectType-Feature-1
content type line 23
Descrição: This paper presents a self-adaptive global best harmony search (SGHS) algorithm for solving continuous optimization problems. In the proposed SGHS algorithm, a new improvisation scheme is developed so that the good information captured in the current global best solution can be well utilized to generate new harmonies. The harmony memory consideration rate ( HMCR) and pitch adjustment rate ( PAR) are dynamically adapted by the learning mechanisms proposed. The distance bandwidth ( BW) is dynamically adjusted to favor exploration in the early stages and exploitation during the final stages of the search process. Extensive computational simulations and comparisons are carried out by employing a set of 16 benchmark problems from literature. The computational results show that the proposed SGHS algorithm is more effective in finding better solutions than the state-of-the-art harmony search (HS) variants.
Editor: Amsterdam: Elsevier Inc
Idioma: Inglês

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A self-adaptive global best harmony search algorithm for continuous optimization problems

Pan, Quan-Ke ; Suganthan, P.N. ; Tasgetiren, M. Fatih ; Liang, J.J.

Amsterdam: Elsevier Inc

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