Improved cuckoo search based neural network learning algorithms for data classification
Artificial Neural Networks (ANN) techniques, mostly Back-Propagation Neural Network (BPNN) algorithm has been used as a tool for recognizing a mapping function among a known set of input and output examples. These networks can be trained with gradient descent back propagation. The algorithm is...
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my-uthm-ep.12102021-09-30T06:28:05Z Improved cuckoo search based neural network learning algorithms for data classification 2014-08 Abdullah, Abdullah QA76 Computer software Artificial Neural Networks (ANN) techniques, mostly Back-Propagation Neural Network (BPNN) algorithm has been used as a tool for recognizing a mapping function among a known set of input and output examples. These networks can be trained with gradient descent back propagation. The algorithm is not definite in finding the global minimum of the error function since gradient descent may get stuck in local minima, where it may stay indefinitely. Among the conventional methods, some researchers prefer Levenberg-Marquardt (LM) because of its convergence speed and performance. On the other hand, LM algorithms which are derivative based algorithms still face a risk of getting stuck in local minima. Recently, a novel meta-heuristic search technique called cuckoo search (CS) has gained a great deal of attention from researchers due to its efficient convergence towards optimal solution. But Cuckoo search is prone to less optimal solution during exploration and exploitation process due to large step lengths taken by CS due to Levy flight. It can also be used to improve the balance between exploration and exploitation of CS algorithm, and to increase the chances of the egg’s survival. This research proposed an improved CS called hybrid Accelerated Cuckoo Particle Swarm Optimization algorithm (HACPSO) with Accelerated particle Swarm Optimization (APSO) algorithm. In the proposed HACPSO algorithm, initially accelerated particle swarm optimization (APSO) algorithm searches within the search space and finds the best sub-search space, and then the CS selects the best nest by traversing the sub-search space. This exploration and exploitation method followed in the proposed HACPSO algorithm makes it to converge to global optima with more efficiency than the original Cuckoo Search (CS) algorithm. Finally, the proposed CS hybrid variants such as; HACPSO, HACPSO-BP, HACPSO-LM, CSBP, CSLM, CSERN, and CSLMERN are evaluated and compared with conventional Back propagation Neural Network (BPNN), Artificial Bee Colony Neural Network (ABCNN), Artificial Bee Colony Back propagation algorithm (ABC-BP), and Artificial Bee Colony Levenberg-Marquardt algorithm (ABC-LM). Specifically, 6 benchmark classification datasets are used for training the hybrid Artificial Neural Network algorithms. Overall from the simulation results, it is realized that the proposed CS based NN algorithms performs better than all other proposed and conventional models in terms of CPU Time, MSE, SD and accuracy. 2014-08 Thesis http://eprints.uthm.edu.my/1210/ http://eprints.uthm.edu.my/1210/1/24p%20ABDULLAH.pdf text en public http://eprints.uthm.edu.my/1210/2/ABDULLAH%20COPYRIGHT%20DECLARATION.pdf text en staffonly http://eprints.uthm.edu.my/1210/3/ABDULLAH%20WATERMARK.pdf text en validuser phd doctoral Universiti Tun Hussein Onn Malaysia Fakulti Sains Komputer dan Teknologi Maklumat |
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Universiti Tun Hussein Onn Malaysia |
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UTHM Institutional Repository |
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English English English |
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QA76 Computer software |
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QA76 Computer software Abdullah, Abdullah Improved cuckoo search based neural network learning algorithms for data classification |
| description |
Artificial Neural Networks (ANN) techniques, mostly Back-Propagation Neural
Network (BPNN) algorithm has been used as a tool for recognizing a mapping
function among a known set of input and output examples. These networks can be
trained with gradient descent back propagation. The algorithm is not definite in
finding the global minimum of the error function since gradient descent may get
stuck in local minima, where it may stay indefinitely. Among the conventional
methods, some researchers prefer Levenberg-Marquardt (LM) because of its
convergence speed and performance. On the other hand, LM algorithms which are
derivative based algorithms still face a risk of getting stuck in local minima.
Recently, a novel meta-heuristic search technique called cuckoo search (CS)
has gained a great deal of attention from researchers due to its efficient convergence
towards optimal solution. But Cuckoo search is prone to less optimal solution during
exploration and exploitation process due to large step lengths taken by CS due to
Levy flight. It can also be used to improve the balance between exploration and
exploitation of CS algorithm, and to increase the chances of the egg’s survival.
This research proposed an improved CS called hybrid Accelerated Cuckoo
Particle Swarm Optimization algorithm (HACPSO) with Accelerated particle Swarm
Optimization (APSO) algorithm. In the proposed HACPSO algorithm, initially
accelerated particle swarm optimization (APSO) algorithm searches within the
search space and finds the best sub-search space, and then the CS selects the best
nest by traversing the sub-search space. This exploration and exploitation method
followed in the proposed HACPSO algorithm makes it to converge to global optima
with more efficiency than the original Cuckoo Search (CS) algorithm. Finally, the proposed CS hybrid variants such as; HACPSO, HACPSO-BP,
HACPSO-LM, CSBP, CSLM, CSERN, and CSLMERN are evaluated and compared
with conventional Back propagation Neural Network (BPNN), Artificial Bee Colony
Neural Network (ABCNN), Artificial Bee Colony Back propagation algorithm
(ABC-BP), and Artificial Bee Colony Levenberg-Marquardt algorithm (ABC-LM).
Specifically, 6 benchmark classification datasets are used for training the hybrid
Artificial Neural Network algorithms. Overall from the simulation results, it is
realized that the proposed CS based NN algorithms performs better than all other
proposed and conventional models in terms of CPU Time, MSE, SD and accuracy. |
| format |
Thesis |
| qualification_name |
Doctor of Philosophy (PhD.) |
| qualification_level |
Doctorate |
| author |
Abdullah, Abdullah |
| author_facet |
Abdullah, Abdullah |
| author_sort |
Abdullah, Abdullah |
| title |
Improved cuckoo search based neural network learning algorithms for data classification |
| title_short |
Improved cuckoo search based neural network learning algorithms for data classification |
| title_full |
Improved cuckoo search based neural network learning algorithms for data classification |
| title_fullStr |
Improved cuckoo search based neural network learning algorithms for data classification |
| title_full_unstemmed |
Improved cuckoo search based neural network learning algorithms for data classification |
| title_sort |
improved cuckoo search based neural network learning algorithms for data classification |
| granting_institution |
Universiti Tun Hussein Onn Malaysia |
| granting_department |
Fakulti Sains Komputer dan Teknologi Maklumat |
| publishDate |
2014 |
| url |
http://eprints.uthm.edu.my/1210/1/24p%20ABDULLAH.pdf http://eprints.uthm.edu.my/1210/2/ABDULLAH%20COPYRIGHT%20DECLARATION.pdf http://eprints.uthm.edu.my/1210/3/ABDULLAH%20WATERMARK.pdf |
| _version_ |
1747830753031356416 |