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

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 gene...

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Bibliographic Details
Published in:Applied mathematics and computation 2010-04, Vol.216 (3), p.830-848
Main Authors: Pan, Quan-Ke, Suganthan, P.N., Tasgetiren, M. Fatih, Liang, J.J.
Format: Article
Language:English
Subjects:
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
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Summary: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.
ISSN:0096-3003
1873-5649
DOI:10.1016/j.amc.2010.01.088