Optimization of High-Dimensional Functions through Hypercube Evaluation
A novel learning algorithm for solving global numerical optimization problems is proposed. The proposed learning algorithm is intense stochastic search method which is based on evaluation and optimization of a hypercube and is called the hypercube optimization (HO) algorithm. The HO algorithm compri...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Hindawi Limited
2015-01-01
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| Series: | Computational Intelligence and Neuroscience |
| Online Access: | http://dx.doi.org/10.1155/2015/967320 |
| Summary: | A novel learning algorithm for solving global numerical optimization problems is proposed. The proposed
learning algorithm is intense stochastic search method which is based on evaluation and optimization of a hypercube and is
called the hypercube optimization (HO) algorithm. The HO algorithm comprises the initialization and evaluation process,
displacement-shrink process, and searching space process. The initialization and evaluation process initializes initial solution
and evaluates the solutions in given hypercube. The displacement-shrink process determines displacement and evaluates
objective functions using new points, and the search area process determines next hypercube using certain rules and evaluates the
new solutions. The algorithms for these processes have been designed and presented in the paper. The designed HO algorithm
is tested on specific benchmark functions. The simulations of HO algorithm have been performed for optimization of functions
of 1000-, 5000-, or even 10000 dimensions. The comparative simulation results with other approaches demonstrate that
the proposed algorithm is a potential candidate for optimization of both low and high dimensional functions. |
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| ISSN: | 1687-5265 1687-5273 |