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Research paper thumbnail of On the Performance of Nature-Inspired Instance Selection Techniques Hybridized with Machine Learning Algorithms for Speed Optimization in Big Dataset Analysis

International Conference on Robotics and Automation, Feb 20, 2020

T he volume of data generated daily through the use of Google search engine, Twitter, Instagram, ... more T he volume of data generated daily through the use of Google search engine, Twitter, Instagram, Facebook, etc. has become overwhelming. Unfortunately, traditional data analytics techniques are fast losing their capabilities and efficiencies handling such volume of data. This challenge has motivated several researchers to design different efficient and robust methods to handle the analysis of big dataset. These techniques include data condensation, divide and conquer, density-based approaches, and distributed computing. Moreover, some of these techniques aim at reducing the volume of input dataset to speed up the computation time of big dataset analysis. Interestingly, faster and more accurate big dataset processing techniques can be developed using Machine Learning (ML) algorithms and Nature-inspired instance selection Techniques. This paper presents two hybrid Nature-Inspired ML-based methods for improving the computation speed of big data analytics. In the first method, we combine a cuckoo search based instance selection technique with four ML algorithms: Naïve Bayes, Random Forest, BayesNet and Artificial Neural Network. Besides, in the second method, we combine a flower pollination based instance selection technique with the four ML algorithms mentioned above. Moreover, we applied the combined methods to five large or medium-scale datasets, and the results show that they significantly improve the computational speed of big data analytics. Furthermore, the results show that Nature-Inspired instance selection techniques have strong data reduction capacity, and they can improve the training speed of ML algorithms, without significantly affecting their classification accuracy.

Research paper thumbnail of The Role of Information and Communication Technologies in Quality Assurance in Open and Distance Learning

Research paper thumbnail of The Role of Information and Communication Technologies in Quality Assurance in Open and Distance Learning

Open and Distance Learning (ODL) is a welcomed innovation and handy tool that could speedily help... more Open and Distance Learning (ODL) is a welcomed innovation and handy tool that could speedily help actualize Education For All. However, despite a long and generally successful track record, ODL is still required to prove that the quality of student learning is at least equivalent to face-to-face teaching so as to promote its value and recognition. With the potentialities provided by modern Information and Communication Technologies (ICTs), their incorporation into Quality Assurance (QA) system can help accomplish this. The paper focuses on issues of using ICT in QA in ODL and some major associated challenges. The role of ICT in QA is very well elaborated in different areas, but a number of related issues necessary to situate the indispensable role of ICT in QA in ODL are given a cursory mention. Some vital recommendations are made to overcome challenges identified including careful and adequate investment by government and private bodies in ICT.

Research paper thumbnail of Studies in particle swarm optimization technique for global optimization

Research paper thumbnail of On the Performance of Linear Decreasing Inertia Weight Particle Swarm Optimization for Global Optimization

The Scientific World Journal, 2013

Linear decreasing inertia weight (LDIW) strategy was introduced to improve on the performance of ... more Linear decreasing inertia weight (LDIW) strategy was introduced to improve on the performance of the original particle swarm optimization (PSO). However, linear decreasing inertia weight PSO (LDIW-PSO) algorithm is known to have the shortcoming of premature convergence in solving complex (multipeak) optimization problems due to lack of enough momentum for particles to do exploitation as the algorithm approaches its terminal point. Researchers have tried to address this shortcoming by modifying LDIW-PSO or proposing new PSO variants. Some of these variants have been claimed to outperform LDIW-PSO. The major goal of this paper is to experimentally establish the fact that LDIW-PSO is very much efficient if its parameters are properly set. First, an experiment was conducted to acquire a percentage value of the search space limits to compute the particle velocity limits in LDIW-PSO based on commonly used benchmark global optimization problems. Second, using the experimentally obtained va...

Research paper thumbnail of Improved Particle Swarm Optimization with a Collective Local Unimodal Search for Continuous Optimization Problems

The Scientific World Journal, 2014

A new local search technique is proposed and used to improve the performance of particle swarm op... more A new local search technique is proposed and used to improve the performance of particle swarm optimization algorithms by addressing the problem of premature convergence. In the proposed local search technique, a potential particle position in the solution search space is collectively constructed by a number of randomly selected particles in the swarm. The number of times the selection is made varies with the dimension of the optimization problem and each selected particle donates the value in the location of its randomly selected dimension from its personal best. After constructing the potential particle position, some local search is done around its neighbourhood in comparison with the current swarm global best position. It is then used to replace the global best particle position if it is found to be better; otherwise no replacement is made. Using some well-studied benchmark problems with low and high dimensions, numerical simulations were used to validate the performance of the ...

Research paper thumbnail of An Adaptive Velocity Particle Swarm Optimization for high-dimensional function optimization

2013 IEEE Congress on Evolutionary Computation, 2013

Research paper thumbnail of Improved Particle Swarm Optimization with a Collective Local Unimodal Search for Continuous Optimization Problems

A new local search technique is proposed and used to improve the performance of particle swarm op... more A new local search technique is proposed and used to improve the performance of particle swarm optimization algorithms by addressing the problem of premature convergence. In the proposed local search technique, a potential particle position in the solution search space is collectively constructed by a number of randomly selected particles in the swarm. The number of times the selection is made varies with the dimension of the optimization problem and each selected particle donates the value in the location of its randomly selected dimension from its personal best. After constructing the potential particle position, some local search is done around its neighbourhood in comparison with the current swarm global best position. It is then used to replace the global best particle position if it is found to be better; otherwise no replacement is made. Using some well-studied benchmark problems with low and high dimensions, numerical simulations were used to validate the performance of the improved algorithms. Comparisons were made with four different PSO variants, two of the variants implement different local search technique while the other two do not. Results show that the improved algorithms could obtain better quality solution while demonstrating better convergence velocity and precision, stability, robustness, and global-local search ability than the competing variants.

Research paper thumbnail of On the Performance of Linear Decreasing Inertia Weight Particle Swarm Optimization for Global Optimization

Linear decreasing inertia weight (LDIW) strategy was introduced to improve on the performance of ... more Linear decreasing inertia weight (LDIW) strategy was introduced to improve on the performance of the original particle swarm optimization (PSO). However, linear decreasing inertia weight PSO (LDIW-PSO) algorithm is known to have the shortcoming of premature convergence in solving complex (multipeak) optimization problems due to lack of enough momentum for particles to do exploitation as the algorithm approaches its terminal point. Researchers have tried to address this shortcoming by modifying LDIW-PSO or proposing new PSO variants. Some of these variants have been claimed to outperform LDIW-PSO. The major goal of this paper is to experimentally establish the fact that LDIW-PSO is very much efficient if its parameters are properly set. First, an experiment was conducted to acquire a percentage value of the search space limits to compute the particle velocity limits in LDIW-PSO based on commonly used benchmark global optimization problems. Second, using the experimentally obtained values, five well-known benchmark optimization problems were used to show the outstanding performance of LDIW-PSO over some of its competitors which have in the past claimed superiority over it. Two other recent PSO variants with different inertia weight strategies were also compared with LDIW-PSO with the latter outperforming both in the simulation experiments conducted.

Research paper thumbnail of On the Performance of Nature-Inspired Instance Selection Techniques Hybridized with Machine Learning Algorithms for Speed Optimization in Big Dataset Analysis

International Conference on Robotics and Automation, Feb 20, 2020

T he volume of data generated daily through the use of Google search engine, Twitter, Instagram, ... more T he volume of data generated daily through the use of Google search engine, Twitter, Instagram, Facebook, etc. has become overwhelming. Unfortunately, traditional data analytics techniques are fast losing their capabilities and efficiencies handling such volume of data. This challenge has motivated several researchers to design different efficient and robust methods to handle the analysis of big dataset. These techniques include data condensation, divide and conquer, density-based approaches, and distributed computing. Moreover, some of these techniques aim at reducing the volume of input dataset to speed up the computation time of big dataset analysis. Interestingly, faster and more accurate big dataset processing techniques can be developed using Machine Learning (ML) algorithms and Nature-inspired instance selection Techniques. This paper presents two hybrid Nature-Inspired ML-based methods for improving the computation speed of big data analytics. In the first method, we combine a cuckoo search based instance selection technique with four ML algorithms: Naïve Bayes, Random Forest, BayesNet and Artificial Neural Network. Besides, in the second method, we combine a flower pollination based instance selection technique with the four ML algorithms mentioned above. Moreover, we applied the combined methods to five large or medium-scale datasets, and the results show that they significantly improve the computational speed of big data analytics. Furthermore, the results show that Nature-Inspired instance selection techniques have strong data reduction capacity, and they can improve the training speed of ML algorithms, without significantly affecting their classification accuracy.

Research paper thumbnail of The Role of Information and Communication Technologies in Quality Assurance in Open and Distance Learning

Research paper thumbnail of The Role of Information and Communication Technologies in Quality Assurance in Open and Distance Learning

Open and Distance Learning (ODL) is a welcomed innovation and handy tool that could speedily help... more Open and Distance Learning (ODL) is a welcomed innovation and handy tool that could speedily help actualize Education For All. However, despite a long and generally successful track record, ODL is still required to prove that the quality of student learning is at least equivalent to face-to-face teaching so as to promote its value and recognition. With the potentialities provided by modern Information and Communication Technologies (ICTs), their incorporation into Quality Assurance (QA) system can help accomplish this. The paper focuses on issues of using ICT in QA in ODL and some major associated challenges. The role of ICT in QA is very well elaborated in different areas, but a number of related issues necessary to situate the indispensable role of ICT in QA in ODL are given a cursory mention. Some vital recommendations are made to overcome challenges identified including careful and adequate investment by government and private bodies in ICT.

Research paper thumbnail of Studies in particle swarm optimization technique for global optimization

Research paper thumbnail of On the Performance of Linear Decreasing Inertia Weight Particle Swarm Optimization for Global Optimization

The Scientific World Journal, 2013

Linear decreasing inertia weight (LDIW) strategy was introduced to improve on the performance of ... more Linear decreasing inertia weight (LDIW) strategy was introduced to improve on the performance of the original particle swarm optimization (PSO). However, linear decreasing inertia weight PSO (LDIW-PSO) algorithm is known to have the shortcoming of premature convergence in solving complex (multipeak) optimization problems due to lack of enough momentum for particles to do exploitation as the algorithm approaches its terminal point. Researchers have tried to address this shortcoming by modifying LDIW-PSO or proposing new PSO variants. Some of these variants have been claimed to outperform LDIW-PSO. The major goal of this paper is to experimentally establish the fact that LDIW-PSO is very much efficient if its parameters are properly set. First, an experiment was conducted to acquire a percentage value of the search space limits to compute the particle velocity limits in LDIW-PSO based on commonly used benchmark global optimization problems. Second, using the experimentally obtained va...

Research paper thumbnail of Improved Particle Swarm Optimization with a Collective Local Unimodal Search for Continuous Optimization Problems

The Scientific World Journal, 2014

A new local search technique is proposed and used to improve the performance of particle swarm op... more A new local search technique is proposed and used to improve the performance of particle swarm optimization algorithms by addressing the problem of premature convergence. In the proposed local search technique, a potential particle position in the solution search space is collectively constructed by a number of randomly selected particles in the swarm. The number of times the selection is made varies with the dimension of the optimization problem and each selected particle donates the value in the location of its randomly selected dimension from its personal best. After constructing the potential particle position, some local search is done around its neighbourhood in comparison with the current swarm global best position. It is then used to replace the global best particle position if it is found to be better; otherwise no replacement is made. Using some well-studied benchmark problems with low and high dimensions, numerical simulations were used to validate the performance of the ...

Research paper thumbnail of An Adaptive Velocity Particle Swarm Optimization for high-dimensional function optimization

2013 IEEE Congress on Evolutionary Computation, 2013

Research paper thumbnail of Improved Particle Swarm Optimization with a Collective Local Unimodal Search for Continuous Optimization Problems

A new local search technique is proposed and used to improve the performance of particle swarm op... more A new local search technique is proposed and used to improve the performance of particle swarm optimization algorithms by addressing the problem of premature convergence. In the proposed local search technique, a potential particle position in the solution search space is collectively constructed by a number of randomly selected particles in the swarm. The number of times the selection is made varies with the dimension of the optimization problem and each selected particle donates the value in the location of its randomly selected dimension from its personal best. After constructing the potential particle position, some local search is done around its neighbourhood in comparison with the current swarm global best position. It is then used to replace the global best particle position if it is found to be better; otherwise no replacement is made. Using some well-studied benchmark problems with low and high dimensions, numerical simulations were used to validate the performance of the improved algorithms. Comparisons were made with four different PSO variants, two of the variants implement different local search technique while the other two do not. Results show that the improved algorithms could obtain better quality solution while demonstrating better convergence velocity and precision, stability, robustness, and global-local search ability than the competing variants.

Research paper thumbnail of On the Performance of Linear Decreasing Inertia Weight Particle Swarm Optimization for Global Optimization

Linear decreasing inertia weight (LDIW) strategy was introduced to improve on the performance of ... more Linear decreasing inertia weight (LDIW) strategy was introduced to improve on the performance of the original particle swarm optimization (PSO). However, linear decreasing inertia weight PSO (LDIW-PSO) algorithm is known to have the shortcoming of premature convergence in solving complex (multipeak) optimization problems due to lack of enough momentum for particles to do exploitation as the algorithm approaches its terminal point. Researchers have tried to address this shortcoming by modifying LDIW-PSO or proposing new PSO variants. Some of these variants have been claimed to outperform LDIW-PSO. The major goal of this paper is to experimentally establish the fact that LDIW-PSO is very much efficient if its parameters are properly set. First, an experiment was conducted to acquire a percentage value of the search space limits to compute the particle velocity limits in LDIW-PSO based on commonly used benchmark global optimization problems. Second, using the experimentally obtained values, five well-known benchmark optimization problems were used to show the outstanding performance of LDIW-PSO over some of its competitors which have in the past claimed superiority over it. Two other recent PSO variants with different inertia weight strategies were also compared with LDIW-PSO with the latter outperforming both in the simulation experiments conducted.