Various algorithms are known for solving linear system of equations. Iteration methods for solving the
large sparse linear systems are recommended. But in the case of general n× m matrices the classic
iterative algorithms are not applicable except for a few cases. The algorithm presented here is based on the
minimization of residual of solution and has some genetic characteristics which require using Genetic
Algorithms. Therefore, this algorithm is best applicable for construction of parallel algorithms. In this
paper, we describe a sequential version of proposed algorithm and present its theoretical analysis.
Moreover we show some numerical results of the sequential algorithm and supply an improved algorithm
and compare the two algorithms.
AN IMPROVED ITERATIVE METHOD FOR SOLVING GENERAL SYSTEM OF EQUATIONS VIA GENE...Zac Darcy
Various algorithms are known for solving linear system of equations. Iteration methods for solving the
large sparse linear systems are recommended. But in the case of general n× m matrices the classic
iterative algorithms are not applicable except for a few cases. The algorithm presented here is based on the
minimization of residual of solution and has some genetic characteristics which require using Genetic
Algorithms. Therefore, this algorithm is best applicable for construction of parallel algorithms. In this
paper, we describe a sequential version of proposed algorithm and present its theoretical analysis.
Moreover we show some numerical results of the sequential algorithm and supply an improved algorithm
and compare the two algorithms.
Presentation is about genetic algorithms. Also it includes introduction to soft computing and hard computing. Hope it serves the purpose and be useful for reference.
Quantum inspired evolutionary algorithm for solving multiple travelling sales...eSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
Genetic algorithm guided key generation in wireless communication (gakg)IJCI JOURNAL
In this paper, the proposed technique use high speed stream cipher approach because this approach is useful where less memory and maximum speed is required for encryption process. In this proposed approach Self Acclimatize Genetic Algorithm based approach is exploits to generate the key stream for encrypt / decrypt the plaintext with the help of key stream. A widely practiced approach to identify a good set of parameters for a problem is through experimentation. For these reasons, proposed enhanced Self Acclimatize Genetic Algorithm (GAKG) offering the most appropriate exploration and exploitation behavior. Parametric tests are done and results are compared with some existing classical techniques, which shows comparable results for the proposed system.
AN IMPROVED ITERATIVE METHOD FOR SOLVING GENERAL SYSTEM OF EQUATIONS VIA GENE...Zac Darcy
Various algorithms are known for solving linear system of equations. Iteration methods for solving the
large sparse linear systems are recommended. But in the case of general n× m matrices the classic
iterative algorithms are not applicable except for a few cases. The algorithm presented here is based on the
minimization of residual of solution and has some genetic characteristics which require using Genetic
Algorithms. Therefore, this algorithm is best applicable for construction of parallel algorithms. In this
paper, we describe a sequential version of proposed algorithm and present its theoretical analysis.
Moreover we show some numerical results of the sequential algorithm and supply an improved algorithm
and compare the two algorithms.
Presentation is about genetic algorithms. Also it includes introduction to soft computing and hard computing. Hope it serves the purpose and be useful for reference.
Quantum inspired evolutionary algorithm for solving multiple travelling sales...eSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
Genetic algorithm guided key generation in wireless communication (gakg)IJCI JOURNAL
In this paper, the proposed technique use high speed stream cipher approach because this approach is useful where less memory and maximum speed is required for encryption process. In this proposed approach Self Acclimatize Genetic Algorithm based approach is exploits to generate the key stream for encrypt / decrypt the plaintext with the help of key stream. A widely practiced approach to identify a good set of parameters for a problem is through experimentation. For these reasons, proposed enhanced Self Acclimatize Genetic Algorithm (GAKG) offering the most appropriate exploration and exploitation behavior. Parametric tests are done and results are compared with some existing classical techniques, which shows comparable results for the proposed system.
This paper research review Ant colony optimization (ACO) and Genetic Algorithm (GA), both are two
powerful meta-heuristics. This paper explains some major defects of these two algorithm at first then
proposes a new model for ACO in which, artificial ants use a quick genetic operator and accelerate their
actions in selecting next state.
Experimental results show that proposed hybrid algorithm is effective and its performance including speed
and accuracy beats other version.
Fabric Textile Defect Detection, By Selection A Suitable Subset Of Wavelet Co...CSCJournals
This paper presents a novel approach for defect detection of fabric textile. For this purpose, First, all wavelet coefficients were extracted from an perfect fabric. But an optimal subset of These coefficients can delete main fabric of image and indicate defects of fabric textile. So we used Genetic Algorithm for finding a suitable subset. The evaluation function in GA was Shannon entropy. Finally, it was shown that we can gain better results for defect detection, by using two separable sets of wavelet coefficients for horizontal and vertical defects. This approach, not only increases accuracy of fabric defect detection, but also, decreases computation time.
A NEW APPROACH IN DYNAMIC TRAVELING SALESMAN PROBLEM: A HYBRID OF ANT COLONY ...ijmpict
Nowadays swarm intelligence-based algorithms are being used widely to optimize the dynamic traveling salesman problem (DTSP). In this paper, we have used mixed method of Ant Colony Optimization (AOC) and gradient descent to optimize DTSP which differs with ACO algorithm in evaporation rate and innovative data. This approach prevents premature convergence and scape from local optimum spots and also makes it possible to find better solutions for algorithm. In this paper, we’re going to offer gradient descent and ACO algorithm which in comparison to some former methods it shows that algorithm has significantly improved routes optimization.
GENETIC ALGORITHM FOR FUNCTION APPROXIMATION: AN EXPERIMENTAL INVESTIGATIONijaia
Function Approximation is a popular engineering problems used in system identification or Equation
optimization. Due to the complex search space it requires, AI techniques has been used extensively to spot
the best curves that match the real behavior of the system. Genetic algorithm is known for their fast
convergence and their ability to find an optimal structure of the solution. We propose using a genetic
algorithm as a function approximator. Our attempt will focus on using the polynomial form of the
approximation. After implementing the algorithm, we are going to report our results and compare it with
the real function output.
MARGINAL PERCEPTRON FOR NON-LINEAR AND MULTI CLASS CLASSIFICATION ijscai
Generalization error of classifier can be reduced by larger margin of separating hyperplane. The proposed classification algorithm implements margin in classical perceptron algorithm, to reduce generalized errors by maximizing margin of separating hyperplane. Algorithm uses the same updation rule with the perceptron, to converge in a finite number of updates to solutions, possessing any desirable fraction of the margin. This solution is again optimized to get maximum possible margin. The algorithm can process linear, non-linear and multi class problems. Experimental results place the proposed classifier equivalent to the support vector machine and even better in some cases. Some preliminary experimental results are briefly discussed.
An Automatic Clustering Technique for Optimal ClustersIJCSEA Journal
This paper proposes a simple, automatic and efficient clustering algorithm, namely, Automatic Merging for Optimal Clusters (AMOC) which aims to generate nearly optimal clusters for the given datasets automatically. The AMOC is an extension to standard k-means with a two phase iterative procedure combining certain validation techniques in order to find optimal clusters with automation of merging of clusters. Experiments on both synthetic and real data have proved that the proposed algorithm finds nearly optimal clustering structures in terms of number of clusters, compactness and separation.
A MODIFIED VORTEX SEARCH ALGORITHM FOR NUMERICAL FUNCTION OPTIMIZATIONijaia
The Vortex Search (VS) algorithm is one of the recently proposed metaheuristic algorithms which was
inspired from the vortical flow of the stirred fluids. Although the VS algorithm is shown to be a good
candidate for the solution of certain optimization problems, it also has some drawbacks. In the VS
algorithm, candidate solutions are generated around the current best solution by using a Gaussian
distribution at each iteration pass. This provides simplicity to the algorithm but it also leads to some
problems along. Especially, for the functions those have a number of local minimum points, to select a
single point to generate candidate solutions leads the algorithm to being trapped into a local minimum
point. Due to the adaptive step-size adjustment scheme used in the VS algorithm, the locality of the created
candidate solutions is increased at each iteration pass. Therefore, if the algorithm cannot escape a local
point as quickly as possible, it becomes much more difficult for the algorithm to escape from that point in
the latter iterations. In this study, a modified Vortex Search algorithm (MVS) is proposed to overcome
above mentioned drawback of the existing VS algorithm. In the MVS algorithm, the candidate solutions
are generated around a number of points at each iteration pass. Computational results showed that with
the help of this modification the global search ability of the existing VS algorithm is improved and the
MVS algorithm outperformed the existing VS algorithm, PSO2011 and ABC algorithms for the benchmark
numerical function set.
CONSTRUCTING A FUZZY NETWORK INTRUSION CLASSIFIER BASED ON DIFFERENTIAL EVOLU...IJCNCJournal
This paper presents a method for constructing intrusion detection systems based on efficient fuzzy rulebased
classifiers. The design process of a fuzzy rule-based classifier from a given input-output data set can
be presented as a feature selection and parameter optimization problem. For parameter optimization of
fuzzy classifiers, the differential evolution is used, while the binary harmonic search algorithm is used for
selection of relevant features. The performance of the designed classifiers is evaluated using the KDD Cup
1999 intrusion detection dataset. The optimal classifier is selected based on the Akaike information
criterion. The optimal intrusion detection system has a 1.21% type I error and a 0.39% type II error. A
comparative study with other methods was accomplished. The results obtained showed the adequacy of the
proposed method
Cost Optimized Design Technique for Pseudo-Random Numbers in Cellular Automataijait
In this research work, we have put an emphasis on the cost effective design approach for high quality pseudo-random numbers using one dimensional Cellular Automata (CA) over Maximum Length CA. This work focuses on different complexities e.g., space complexity, time complexity, design complexity and searching complexity for the generation of pseudo-random numbers in CA. The optimization procedure for
these associated complexities is commonly referred as the cost effective generation approach for pseudorandom numbers. The mathematical approach for proposed methodology over the existing maximum length CA emphasizes on better flexibility to fault coverage. The randomness quality of the generated patterns for the proposed methodology has been verified using Diehard Tests which reflects that the randomness quality
achieved for proposed methodology is equal to the quality of randomness of the patterns generated by the maximum length cellular automata. The cost effectiveness results a cheap hardware implementation for the concerned pseudo-random pattern generator. Short version of this paper has been published in [1].
The International Journal of Engineering and Science (The IJES)theijes
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
COMPARING THE CUCKOO ALGORITHM WITH OTHER ALGORITHMS FOR ESTIMATING TWO GLSD ...csandit
This study introduces and compares different methods for estimating the two parameters of
generalized logarithmic series distribution. These methods are the cuckoo search optimization,
maximum likelihood estimation, and method of moments algorithms. All the required
derivations and basic steps of each algorithm are explained. The applications for these
algorithms are implemented through simulations using different sample sizes (n = 15, 25, 50,
100). Results are compared using the statistical measure mean square error.
MODIFIED VORTEX SEARCH ALGORITHM FOR REAL PARAMETER OPTIMIZATIONcscpconf
The Vortex Search (VS) algorithm is one of the recently proposed metaheuristic algorithms which was inspired from the vortical flow of the stirred fluids. Although the VS algorithm is
shown to be a good candidate for the solution of certain optimization problems, it also has some drawbacks. In the VS algorithm, candidate solutions are generated around the current best solution by using a Gaussian distribution at each iteration pass. This provides simplicity to the
algorithm but it also leads to some problems along. Especially, for the functions those have a number of local minimum points, to select a single point to generate candidate solutions leads the algorithm to being trapped into a local minimum point. Due to the adaptive step-size
adjustment scheme used in the VS algorithm, the locality of the created candidate solutions is increased at each iteration pass. Therefore, if the algorithm cannot escape a local point as
quickly as possible, it becomes much more difficult for the algorithm to escape from that point
in the latter iterations. In this study, a modified Vortex Search algorithm (MVS) is proposed to
overcome above mentioned drawback of the existing VS algorithm. In the MVS algorithm, the candidate solutions are generated around a number of points at each iteration pass. Computational results showed that with the help of this modification the global search ability of
the existing VS algorithm is improved and the MVS algorithm outperformed the existing VS algorithm, PSO2011 and ABC algorithms for the benchmark numerical function set.
Modified Vortex Search Algorithm for Real Parameter Optimization csandit
The Vortex Search (VS) algorithm is one of the rece
ntly proposed metaheuristic algorithms
which was inspired from the vortical flow of the st
irred fluids. Although the VS algorithm is
shown to be a good candidate for the solution of ce
rtain optimization problems, it also has some
drawbacks. In the VS algorithm, candidate solutions
are generated around the current best
solution by using a Gaussian distribution at each i
teration pass. This provides simplicity to the
algorithm but it also leads to some problems along.
Especially, for the functions those have a
number of local minimum points, to select a single
point to generate candidate solutions leads
the algorithm to being trapped into a local minimum
point. Due to the adaptive step-size
adjustment scheme used in the VS algorithm, the loc
ality of the created candidate solutions is
increased at each iteration pass. Therefore, if the
algorithm cannot escape a local point as
quickly as possible, it becomes much more difficult
for the algorithm to escape from that point
in the latter iterations. In this study, a modified
Vortex Search algorithm (MVS) is proposed to
overcome above mentioned drawback of the existing V
S algorithm. In the MVS algorithm, the
candidate solutions are generated around a number o
f points at each iteration pass.
Computational results showed that with the help of
this modification the global search ability of
the existing VS algorithm is improved and the MVS a
lgorithm outperformed the existing VS
algorithm, PSO2011 and ABC algorithms for the bench
mark numerical function set
A New Method Based on MDA to Enhance the Face Recognition PerformanceCSCJournals
A novel tensor based method is prepared to solve the supervised dimensionality reduction problem. In this paper a multilinear principal component analysis(MPCA) is utilized to reduce the tensor object dimension then a multilinear discriminant analysis(MDA), is applied to find the best subspaces. Because the number of possible subspace dimensions for any kind of tensor objects is extremely high, so testing all of them for finding the best one is not feasible. So this paper also presented a method to solve that problem, The main criterion of algorithm is not similar to Sequential mode truncation(SMT) and full projection is used to initialize the iterative solution and find the best dimension for MDA. This paper is saving the extra times that we should spend to find the best dimension. So the execution time will be decreasing so much. It should be noted that both of the algorithms work with tensor objects with the same order so the structure of the objects has been never broken. Therefore the performance of this method is getting better. The advantage of these algorithms is avoiding the curse of dimensionality and having a better performance in the cases with small sample sizes. Finally, some experiments on ORL and CMPU-PIE databases is provided.
Hybrid optimization of pumped hydro system and solar- Engr. Abdul-Azeez.pdffxintegritypublishin
Advancements in technology unveil a myriad of electrical and electronic breakthroughs geared towards efficiently harnessing limited resources to meet human energy demands. The optimization of hybrid solar PV panels and pumped hydro energy supply systems plays a pivotal role in utilizing natural resources effectively. This initiative not only benefits humanity but also fosters environmental sustainability. The study investigated the design optimization of these hybrid systems, focusing on understanding solar radiation patterns, identifying geographical influences on solar radiation, formulating a mathematical model for system optimization, and determining the optimal configuration of PV panels and pumped hydro storage. Through a comparative analysis approach and eight weeks of data collection, the study addressed key research questions related to solar radiation patterns and optimal system design. The findings highlighted regions with heightened solar radiation levels, showcasing substantial potential for power generation and emphasizing the system's efficiency. Optimizing system design significantly boosted power generation, promoted renewable energy utilization, and enhanced energy storage capacity. The study underscored the benefits of optimizing hybrid solar PV panels and pumped hydro energy supply systems for sustainable energy usage. Optimizing the design of solar PV panels and pumped hydro energy supply systems as examined across diverse climatic conditions in a developing country, not only enhances power generation but also improves the integration of renewable energy sources and boosts energy storage capacities, particularly beneficial for less economically prosperous regions. Additionally, the study provides valuable insights for advancing energy research in economically viable areas. Recommendations included conducting site-specific assessments, utilizing advanced modeling tools, implementing regular maintenance protocols, and enhancing communication among system components.
Quality defects in TMT Bars, Possible causes and Potential Solutions.PrashantGoswami42
Maintaining high-quality standards in the production of TMT bars is crucial for ensuring structural integrity in construction. Addressing common defects through careful monitoring, standardized processes, and advanced technology can significantly improve the quality of TMT bars. Continuous training and adherence to quality control measures will also play a pivotal role in minimizing these defects.
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This paper research review Ant colony optimization (ACO) and Genetic Algorithm (GA), both are two
powerful meta-heuristics. This paper explains some major defects of these two algorithm at first then
proposes a new model for ACO in which, artificial ants use a quick genetic operator and accelerate their
actions in selecting next state.
Experimental results show that proposed hybrid algorithm is effective and its performance including speed
and accuracy beats other version.
Fabric Textile Defect Detection, By Selection A Suitable Subset Of Wavelet Co...CSCJournals
This paper presents a novel approach for defect detection of fabric textile. For this purpose, First, all wavelet coefficients were extracted from an perfect fabric. But an optimal subset of These coefficients can delete main fabric of image and indicate defects of fabric textile. So we used Genetic Algorithm for finding a suitable subset. The evaluation function in GA was Shannon entropy. Finally, it was shown that we can gain better results for defect detection, by using two separable sets of wavelet coefficients for horizontal and vertical defects. This approach, not only increases accuracy of fabric defect detection, but also, decreases computation time.
A NEW APPROACH IN DYNAMIC TRAVELING SALESMAN PROBLEM: A HYBRID OF ANT COLONY ...ijmpict
Nowadays swarm intelligence-based algorithms are being used widely to optimize the dynamic traveling salesman problem (DTSP). In this paper, we have used mixed method of Ant Colony Optimization (AOC) and gradient descent to optimize DTSP which differs with ACO algorithm in evaporation rate and innovative data. This approach prevents premature convergence and scape from local optimum spots and also makes it possible to find better solutions for algorithm. In this paper, we’re going to offer gradient descent and ACO algorithm which in comparison to some former methods it shows that algorithm has significantly improved routes optimization.
GENETIC ALGORITHM FOR FUNCTION APPROXIMATION: AN EXPERIMENTAL INVESTIGATIONijaia
Function Approximation is a popular engineering problems used in system identification or Equation
optimization. Due to the complex search space it requires, AI techniques has been used extensively to spot
the best curves that match the real behavior of the system. Genetic algorithm is known for their fast
convergence and their ability to find an optimal structure of the solution. We propose using a genetic
algorithm as a function approximator. Our attempt will focus on using the polynomial form of the
approximation. After implementing the algorithm, we are going to report our results and compare it with
the real function output.
MARGINAL PERCEPTRON FOR NON-LINEAR AND MULTI CLASS CLASSIFICATION ijscai
Generalization error of classifier can be reduced by larger margin of separating hyperplane. The proposed classification algorithm implements margin in classical perceptron algorithm, to reduce generalized errors by maximizing margin of separating hyperplane. Algorithm uses the same updation rule with the perceptron, to converge in a finite number of updates to solutions, possessing any desirable fraction of the margin. This solution is again optimized to get maximum possible margin. The algorithm can process linear, non-linear and multi class problems. Experimental results place the proposed classifier equivalent to the support vector machine and even better in some cases. Some preliminary experimental results are briefly discussed.
An Automatic Clustering Technique for Optimal ClustersIJCSEA Journal
This paper proposes a simple, automatic and efficient clustering algorithm, namely, Automatic Merging for Optimal Clusters (AMOC) which aims to generate nearly optimal clusters for the given datasets automatically. The AMOC is an extension to standard k-means with a two phase iterative procedure combining certain validation techniques in order to find optimal clusters with automation of merging of clusters. Experiments on both synthetic and real data have proved that the proposed algorithm finds nearly optimal clustering structures in terms of number of clusters, compactness and separation.
A MODIFIED VORTEX SEARCH ALGORITHM FOR NUMERICAL FUNCTION OPTIMIZATIONijaia
The Vortex Search (VS) algorithm is one of the recently proposed metaheuristic algorithms which was
inspired from the vortical flow of the stirred fluids. Although the VS algorithm is shown to be a good
candidate for the solution of certain optimization problems, it also has some drawbacks. In the VS
algorithm, candidate solutions are generated around the current best solution by using a Gaussian
distribution at each iteration pass. This provides simplicity to the algorithm but it also leads to some
problems along. Especially, for the functions those have a number of local minimum points, to select a
single point to generate candidate solutions leads the algorithm to being trapped into a local minimum
point. Due to the adaptive step-size adjustment scheme used in the VS algorithm, the locality of the created
candidate solutions is increased at each iteration pass. Therefore, if the algorithm cannot escape a local
point as quickly as possible, it becomes much more difficult for the algorithm to escape from that point in
the latter iterations. In this study, a modified Vortex Search algorithm (MVS) is proposed to overcome
above mentioned drawback of the existing VS algorithm. In the MVS algorithm, the candidate solutions
are generated around a number of points at each iteration pass. Computational results showed that with
the help of this modification the global search ability of the existing VS algorithm is improved and the
MVS algorithm outperformed the existing VS algorithm, PSO2011 and ABC algorithms for the benchmark
numerical function set.
CONSTRUCTING A FUZZY NETWORK INTRUSION CLASSIFIER BASED ON DIFFERENTIAL EVOLU...IJCNCJournal
This paper presents a method for constructing intrusion detection systems based on efficient fuzzy rulebased
classifiers. The design process of a fuzzy rule-based classifier from a given input-output data set can
be presented as a feature selection and parameter optimization problem. For parameter optimization of
fuzzy classifiers, the differential evolution is used, while the binary harmonic search algorithm is used for
selection of relevant features. The performance of the designed classifiers is evaluated using the KDD Cup
1999 intrusion detection dataset. The optimal classifier is selected based on the Akaike information
criterion. The optimal intrusion detection system has a 1.21% type I error and a 0.39% type II error. A
comparative study with other methods was accomplished. The results obtained showed the adequacy of the
proposed method
Cost Optimized Design Technique for Pseudo-Random Numbers in Cellular Automataijait
In this research work, we have put an emphasis on the cost effective design approach for high quality pseudo-random numbers using one dimensional Cellular Automata (CA) over Maximum Length CA. This work focuses on different complexities e.g., space complexity, time complexity, design complexity and searching complexity for the generation of pseudo-random numbers in CA. The optimization procedure for
these associated complexities is commonly referred as the cost effective generation approach for pseudorandom numbers. The mathematical approach for proposed methodology over the existing maximum length CA emphasizes on better flexibility to fault coverage. The randomness quality of the generated patterns for the proposed methodology has been verified using Diehard Tests which reflects that the randomness quality
achieved for proposed methodology is equal to the quality of randomness of the patterns generated by the maximum length cellular automata. The cost effectiveness results a cheap hardware implementation for the concerned pseudo-random pattern generator. Short version of this paper has been published in [1].
The International Journal of Engineering and Science (The IJES)theijes
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
COMPARING THE CUCKOO ALGORITHM WITH OTHER ALGORITHMS FOR ESTIMATING TWO GLSD ...csandit
This study introduces and compares different methods for estimating the two parameters of
generalized logarithmic series distribution. These methods are the cuckoo search optimization,
maximum likelihood estimation, and method of moments algorithms. All the required
derivations and basic steps of each algorithm are explained. The applications for these
algorithms are implemented through simulations using different sample sizes (n = 15, 25, 50,
100). Results are compared using the statistical measure mean square error.
MODIFIED VORTEX SEARCH ALGORITHM FOR REAL PARAMETER OPTIMIZATIONcscpconf
The Vortex Search (VS) algorithm is one of the recently proposed metaheuristic algorithms which was inspired from the vortical flow of the stirred fluids. Although the VS algorithm is
shown to be a good candidate for the solution of certain optimization problems, it also has some drawbacks. In the VS algorithm, candidate solutions are generated around the current best solution by using a Gaussian distribution at each iteration pass. This provides simplicity to the
algorithm but it also leads to some problems along. Especially, for the functions those have a number of local minimum points, to select a single point to generate candidate solutions leads the algorithm to being trapped into a local minimum point. Due to the adaptive step-size
adjustment scheme used in the VS algorithm, the locality of the created candidate solutions is increased at each iteration pass. Therefore, if the algorithm cannot escape a local point as
quickly as possible, it becomes much more difficult for the algorithm to escape from that point
in the latter iterations. In this study, a modified Vortex Search algorithm (MVS) is proposed to
overcome above mentioned drawback of the existing VS algorithm. In the MVS algorithm, the candidate solutions are generated around a number of points at each iteration pass. Computational results showed that with the help of this modification the global search ability of
the existing VS algorithm is improved and the MVS algorithm outperformed the existing VS algorithm, PSO2011 and ABC algorithms for the benchmark numerical function set.
Modified Vortex Search Algorithm for Real Parameter Optimization csandit
The Vortex Search (VS) algorithm is one of the rece
ntly proposed metaheuristic algorithms
which was inspired from the vortical flow of the st
irred fluids. Although the VS algorithm is
shown to be a good candidate for the solution of ce
rtain optimization problems, it also has some
drawbacks. In the VS algorithm, candidate solutions
are generated around the current best
solution by using a Gaussian distribution at each i
teration pass. This provides simplicity to the
algorithm but it also leads to some problems along.
Especially, for the functions those have a
number of local minimum points, to select a single
point to generate candidate solutions leads
the algorithm to being trapped into a local minimum
point. Due to the adaptive step-size
adjustment scheme used in the VS algorithm, the loc
ality of the created candidate solutions is
increased at each iteration pass. Therefore, if the
algorithm cannot escape a local point as
quickly as possible, it becomes much more difficult
for the algorithm to escape from that point
in the latter iterations. In this study, a modified
Vortex Search algorithm (MVS) is proposed to
overcome above mentioned drawback of the existing V
S algorithm. In the MVS algorithm, the
candidate solutions are generated around a number o
f points at each iteration pass.
Computational results showed that with the help of
this modification the global search ability of
the existing VS algorithm is improved and the MVS a
lgorithm outperformed the existing VS
algorithm, PSO2011 and ABC algorithms for the bench
mark numerical function set
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A novel tensor based method is prepared to solve the supervised dimensionality reduction problem. In this paper a multilinear principal component analysis(MPCA) is utilized to reduce the tensor object dimension then a multilinear discriminant analysis(MDA), is applied to find the best subspaces. Because the number of possible subspace dimensions for any kind of tensor objects is extremely high, so testing all of them for finding the best one is not feasible. So this paper also presented a method to solve that problem, The main criterion of algorithm is not similar to Sequential mode truncation(SMT) and full projection is used to initialize the iterative solution and find the best dimension for MDA. This paper is saving the extra times that we should spend to find the best dimension. So the execution time will be decreasing so much. It should be noted that both of the algorithms work with tensor objects with the same order so the structure of the objects has been never broken. Therefore the performance of this method is getting better. The advantage of these algorithms is avoiding the curse of dimensionality and having a better performance in the cases with small sample sizes. Finally, some experiments on ORL and CMPU-PIE databases is provided.
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Hybrid optimization of pumped hydro system and solar- Engr. Abdul-Azeez.pdffxintegritypublishin
Advancements in technology unveil a myriad of electrical and electronic breakthroughs geared towards efficiently harnessing limited resources to meet human energy demands. The optimization of hybrid solar PV panels and pumped hydro energy supply systems plays a pivotal role in utilizing natural resources effectively. This initiative not only benefits humanity but also fosters environmental sustainability. The study investigated the design optimization of these hybrid systems, focusing on understanding solar radiation patterns, identifying geographical influences on solar radiation, formulating a mathematical model for system optimization, and determining the optimal configuration of PV panels and pumped hydro storage. Through a comparative analysis approach and eight weeks of data collection, the study addressed key research questions related to solar radiation patterns and optimal system design. The findings highlighted regions with heightened solar radiation levels, showcasing substantial potential for power generation and emphasizing the system's efficiency. Optimizing system design significantly boosted power generation, promoted renewable energy utilization, and enhanced energy storage capacity. The study underscored the benefits of optimizing hybrid solar PV panels and pumped hydro energy supply systems for sustainable energy usage. Optimizing the design of solar PV panels and pumped hydro energy supply systems as examined across diverse climatic conditions in a developing country, not only enhances power generation but also improves the integration of renewable energy sources and boosts energy storage capacities, particularly beneficial for less economically prosperous regions. Additionally, the study provides valuable insights for advancing energy research in economically viable areas. Recommendations included conducting site-specific assessments, utilizing advanced modeling tools, implementing regular maintenance protocols, and enhancing communication among system components.
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Maintaining high-quality standards in the production of TMT bars is crucial for ensuring structural integrity in construction. Addressing common defects through careful monitoring, standardized processes, and advanced technology can significantly improve the quality of TMT bars. Continuous training and adherence to quality control measures will also play a pivotal role in minimizing these defects.
Democratizing Fuzzing at Scale by Abhishek Aryaabh.arya
Presented at NUS: Fuzzing and Software Security Summer School 2024
This keynote talks about the democratization of fuzzing at scale, highlighting the collaboration between open source communities, academia, and industry to advance the field of fuzzing. It delves into the history of fuzzing, the development of scalable fuzzing platforms, and the empowerment of community-driven research. The talk will further discuss recent advancements leveraging AI/ML and offer insights into the future evolution of the fuzzing landscape.
About
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
• Remote control: Parallel or serial interface.
• Compatible with MAFI CCR system.
• Compatible with IDM8000 CCR.
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
• Easy in configuration using DIP switches.
Technical Specifications
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
Key Features
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
• Remote control: Parallel or serial interface
• Compatible with MAFI CCR system
• Copatiable with IDM8000 CCR
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
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An Improved Iterative Method for Solving General System of Equations via Genetic Algorithms
1. International Journal of Information Technology, Modeling and Computing (IJITMC) Vol. 4, No.1, February 2016
DOI : 10.5121/ijitmc.2016.4104 35
AN IMPROVED ITERATIVE METHOD FOR SOLVING
GENERAL SYSTEM OF EQUATIONS VIA GENETIC
ALGORITHMS
Seyed Abolfazl Shahzadehfazeli1, 2
Zainab Haji Abootorabi2,3
1
Parallel Processing Laboratory, Yazd University, Yazd, Iran
2
Department of Computer Science, Faculty of Mathematics, Yazd University,Yazd, Iran
3
Department of Mathematics, PNU University, Yazd, Iran
ABSTRACT
Various algorithms are known for solving linear system of equations. Iteration methods for solving the
large sparse linear systems are recommended. But in the case of general n× m matrices the classic
iterative algorithms are not applicable except for a few cases. The algorithm presented here is based on the
minimization of residual of solution and has some genetic characteristics which require using Genetic
Algorithms. Therefore, this algorithm is best applicable for construction of parallel algorithms. In this
paper, we describe a sequential version of proposed algorithm and present its theoretical analysis.
Moreover we show some numerical results of the sequential algorithm and supply an improved algorithm
and compare the two algorithms.
Keywords
Large sparse linear systems, Iterative Genetic algorithms, Parallel algorithm.
1. INTRODUCTION
Let A be a general n ×m matrix. The main problem is to solve the linear system of equations:
Ax = b (1)
where x∈Rm
and b∈Rn
are the solution and the given right hand side vectors. We can determine
from matrix A and the vector b, the existence and uniqueness of the solution of (1). Theoretically
the Gaussian or Gauss-Jordan elimination algorithm is an appropriate tool to solve the system (1)
and to decide the question of solvability. when we use floating point arithmetic for large
systems, these direct algorithms are inapplicable. For these cases the iterative algorithms are
suitable. Effective iterative algorithms are known for symmetric positive definite linear systems.
In general, iterative algorithms can be written in the form of:
x(n)=B x(n−1)+d, n=1, 2,... (2)
where B and d are such a matrix and vector that make stationary solution of (2) equivalent with
(1), see ([1]). These iterative algorithms can be applied for general non symmetric linear systems
as well, if we solve the following normal system:
AT
Ax = AT
b = v (3)
2. International Journal of Information Technology, Modeling and Computing (IJITMC) Vol. 4, No.1, February 2016
36
instead of the original one. A disadvantage of this approach is that the resulting linear system (3)
for matrices with full rank will be Hermitian ones, however, its condition number will be the
square of the original condition number. Therefore, the convergence will be very slow. For
general linear systems when A is non-Hermitian, instead of using some variant of the Conjugate
Gradient (CG) algorithms, one of the most successful schemes is the generalized minimal residual
algorithm (GMRES), see ([9, 10]) and the biconjugate gradient algorithm (BCG) see ([2]).
A more effective approach was suggested by Freund and Nachtigal ([5]) for the case of general
nonsingular non-Hermitian systems which is called the quasi minimal residual algorithm (QMR).
An iterative minimal residual algorithm which is slightly different from the above ones uses
Genetic Algorithms (GA), see ([4, 6,7, 8]).
In the following, we describe an improved method using genetic algorithms, in which, the initial
population is larger, uses a broader search field and its crossover operator on initial population
enhances the algorithm convergence speed. Generally, genetic algorithm with larger search space,
does not guarantee the convergence speed see ([3]).
In this paper, it is shown that our improved method is in practice much faster than previous
types. This advantage can be very important for development of these algorithms for parallel
processing. The result obtained in [8] is briefly reviewed here to clarify the improved algorithm.
2. AN ITERATIVE MINIMAL RESIDUAL ALGORITHM
The most of iterative algorithms for solving linear systems are based on some minimization
algorithm. We can obtain the normal system (3) in the following way by the least square
minimization. We have to solve the following problem:
2
2
2
2
min
)
,
(
min
)
,
(
min
min r
r
r
b
Ax
b
Ax
b
Ax m
n
n
R
r
R
x
R
x ∈
∈
∈
=
=
−
−
=
− (4)
where r=Ax−b is the residual of the vector x.
It is easy to show that the equation (4) can be written as in (3). More precisely, the necessary
condition for the existence and uniqueness of the solution of (4) is obtained for the fulfillment of
(3). The Hermitian property of the normal matrix AT
A is a sufficient condition for the
uniqueness. For general non-Hermitian matrices this condition is not fulfilled in general. One
possible algorithm to solve the problem (4) can be obtained from the following theorem.
Theorem 1. Let n
m
R
R
A →
∈ and n
R
b∈ be arbitrary matrix and vector. Moreover, let
m
R
x ∈
α
and m
R
x ∈
β
be arbitrary different vectors for which ( ) 0
≠
− β
α
x
x
A .
Let us introduce the following notations:
,
b
Ax
r s
s
−
= β
α ,
=
S
and
β
α
β
α
x
c
cx
X )
1
(
,
−
+
= , β
α
β
α
r
c
cr
r )
(
,
−
+
= 1
where R
c∈ . We have β
α
β
α ,
,
r
b
Ax =
− . Then, the solution of the minimization problem of
(4) is the vector β
α ,
x with c, where
3. International Journal of Information Technology, Modeling and Computing (IJITMC) Vol. 4, No.1, February 2016
37
2
2
)
,
(
β
α
β
α
β
r
r
r
r
r
c
−
−
=
Moreover,
{ }
2
2
,
2
,
,
min β
α
β
α
β
α
r
r
r 〈 .
The Algorithm 1
From Theorem 1 we obtain an algorithm (see [8]), which generates an approximate solution
sequence k
x , k=1, 2, 3,... with residual vectors k
r , k=1, 2, 3,.....
1) Let x1
be an arbitrary vector and ε the tolerance.
2) Calculate r1
=Ax1
−b.
3) Generate an arbitrary vector, x2
such that r1
−r2
≠ 0.
4) Calculate the c1,2
.
5) Calculate the new
x1,2
:=c 1,2
x1
+(1−c1,2
) x2
and r1,2
:=c 1,2
r1
+(1−c1,2
) r2
vectors.
6) x1
:= x1,2
and r1
:=r1,2
.
7) If r1
< ε then go to 8, else go to 3.
8 )The approximate solution is x1
.
9)End of algorithm.
The simplest algorithm which can be obtained from Theorem 1 is the algorithm 1. Therefore, this
algorithm does not converge faster than the classical ones.
3. THE IMPROVED ALGORITHM USING GA
Genetic algorithms (GAs) were proposed first time by John Holland and were developed by
Holland and his colleagues at the University of Michigan in the 1960s and the 1970s. On
continuous and discrete combinatorial problems, GAs work very well. But they tend to be
computationally expensive. GAs are examples of algorithms that are used in this field and have
improved tremendously in the past two decades. A genetic algorithm (or GA) is a search
technique used in computing to find true or approximate solutions to optimization and search
problems. (GA)s are in the class of global search heuristics. (GA)s are a particular class of
evolutionary algorithms that use techniques inspired by evolutionary biology such as inheritance,
selection, crossover and mutation.
Selection: Choice of individual genomes from a population for using the crossover operator is the
stage of a genetic algorithm which is called Selection. There are many ways how to select the
best chromosomes, for example roulette wheel selection, Boltzman selection, tournament
selection, rank selection, steady state selection and some others.
Crossover: After we have decided what encoding we will use, we can make a step to crossover.
Crossover selects genes from parent chromosomes and creates a new offspring. The simplest way
how to do this is to choose randomly some crossover point and everything before this point copy
from a first parent and then everything after a crossover point copy from the second parent. There
are many methods how to do crossover. For example Single point crossover, Two point
crossover, Uniform crossover and Arithmetic crossover.
4. International Journal of Information Technology, Modeling and Computing (IJITMC) Vol. 4, No.1, February 2016
38
Mutation: After a crossover is performed, mutation takes place. This is to prevent falling all
solutions in population into a local optimum of solved problem. Mutation changes randomly the
new offspring. As well as the crossover, the mutation depends on the encoding . For example
mutation could be exchanging two genes, when we are encoding permutations. For binary
encoding we can switch a few randomly chosen bits from 1 to 0 or from 0 to 1.
The most important parts of the genetic algorithm are the crossover and mutation. The
performance is influenced mainly by these two operators. Crossover and mutation are two basic
operators of GA and performance of GA is very dependent on them. Implementation and type of
operators depends on a given problem and encoding.
The evolution usually starts from a population of randomly generated individuals and happens in
generations. The fitness of every individual in the population, evaluate in each generation and
select multiple individuals from the current population and modify to form a new population. In
the next iteration of the algorithm use the new population. The algorithm terminates when either a
maximum number of generations has been produced, or a satisfactory fitness level has been
reached for the population.
The Basic Genetic Algorithm
1) Generate random population of n chromosomes.
2) Evaluate the fitness function of each chromosome x in the population.
3) Create a new population by repeating following steps until the new population is
complete.
a) Selection: Select two parent chromosomes from a population according to their fitness.
b) Crossover: With a crossover probability crossover the parents to form a new
offspring (children). If no crossover was performed, offspring is an exact copy of
parents.
c) Mutation: With a mutation probability mutate new offspring at each locus.
d) Place new offspring in a new population.
4) Use new generated population for a further run of algorithm.
5) If the end condition is satisfied, stop, and return the best solution in current
population.
6) Go to step 2
The three most important aspects of using genetic algorithms are:
1) Definition of the objective function.
2) Definition and implementation of the genetic representation.
3) Definition and implementation of the genetic operators. Once these three have been
defined, the generic algorithm should work fairly well.
In algorithm 1, we choose x1
and x2
arbitrarily, then use crossover operator to reach an optimal x1,2
and replace it for x1
. Then, we randomly select x2
again. Finally this process is continued until a
fairly accurate approximation to the answer is achieved for linear equations Ax=b. But in the
improved algorithm, instead of x1
and x2
and instead of the original population from two-parent,
m-parent is chosen. (Note in the allocate names, x1
, x2
,..., xm
, m is the number of columns of
matrix A).
The crossover operator performed on the initial population generates vectors, x1,2
,..., xm-1,m
. This
process is repeatedly performed on the newly generated vectors until a single vector x1,2,3,…,m
as
5. International Journal of Information Technology, Modeling and Computing (IJITMC) Vol. 4, No.1, February 2016
39
an approximate initial solution is obtained. This is now replaced by x1
also we randomly select
x2
,..., xm
again for second population and the algorithm is repeated again and again until a close
solution is obtained. The following table shows how the new vectors are generated. For detail
refer to the algorithm 2.
x1
x1,2
x2
x1,2,3
x2,3
x3
x2,3,4
…
...
x1,2,3,...,m
…
…
xm-1,m
xm
Now the algorithm 1 is improved in order to increase the convergence speed.
The Algorithm 2
1) Let x1
be an arbitrary vector and ε the error tolerance and i=1.
2) Calculate r1
=Ax1
−b.
3) Generate an arbitrary vector , x2
,…, xm
such that ri
−rj
≠ 0 , )
( j
i ≠ و m
j
i ,...,
1
, = .
4) Calculate the
2
2
1
1
1
)
,
(
k
k
k
k
k
k
r
r
r
r
r
C
−
−
=
+
+
+
, for 1
,...,
2
, −
= m
i
k .
5) Calculate the new
xk,k+1
= Ck xk
+(1-Ck) xk+1
and rk,k+1
= Ck rk
+(1-Ck) rk+1
vectors, for 1
,..., −
= m
i
k .
6) 1
1 +
+
= k
k
k
x
x ,
, for k= i,…,n-1, and 1
+
= i
i .
7) If i=n-1, then m
m
x
x ,
1
1 −
= and m
m
r
r ,
1
1 −
= else go to 4.
8) If ε
<
2
1
r then go to 9, else go to 3.
9) The approximate solution is x1
.
10) End of algorithm.
4. NUMERICAL EXPERIMENTS
In this section, we compare algorithm 1 and algorithm 2. Also, we use the different examples and
review speed of the algorithm and we show some examples in the summary table and an example
to bring more detail.
In the examples, the condition number of the matrices A are chosen rather small (The coefficient
matrices are well-conditioned).
The following table (table1) compares the number of iterations by the two algorithms.
Figure 1 shows that for matrix A1 with the condition number 80.903 and spectral radius 15.7009,
the algorithm 1 converges after 136720 iterations while the number of iterations in the improved
algorithm (algorithm 2) is 16129. This is a notable reduction.
6. International Journal of Information Technology, Modeling and Computing (IJITMC) Vol. 4, No.1, February 2016
40
Table1. The number of iterations.
No. of iter.
algorithm 2
No. of iter.
algorithm1
Tol.
Dim.
Matrix
16129
136720
10-3
20
15×
A1
1812
10691
10-3
15
20×
A2
8285
52273
10-3
25
20×
A3
279
665
10-3
20
25×
A4
22920
119041
10-3
30
25×
A5
349
805
10-3
25
30×
A6
436
1228
10-3
30
35×
A7
500
1390
10-3
35
40×
A8
Figure 1. Speed of convergence of the Algorithm 1 and Algorithm 2 on the A1 matrix.
0 2 4 6 8 10 12 14
x 10
4
10
-3
10
-2
10
-1
10
0
10
1
10
2
10
3
iteration
residual
norm
Algo.1
Algo.2
7. International Journal of Information Technology, Modeling and Computing (IJITMC) Vol. 4, No.1, February 2016
41
5. CONCLUSION
In this paper, for solving systems of linear equations an improved algorithm is presented. In
contradiction to other iterative methods (Jacobi, Gauss-Seidel, conjugate gradient and even
Gauss-elimination methods), this method has not any limitations.
Genetic algorithm enhances an appropriate response to eliminate restrictions and is a simple
method for obtaining the solution. As the examples show, the number of iterations in algorithm 2
is incredibly reduced. The merit of the algorithm is its simplicity to use specially for non-square
systems and to extend to large systems of equations by incorporating parallel computing.
REFERENCES
[1] Hageman L. A. & Joung D. M., (1981) Applied Iterative Methods, Computer Science and Applied
Mathematics, Academic Press.
[2] Hestenes M.R. & Stiefel,E. (1954) Methods of conjugate gradients for solving linear systems; J. Res.
Natl. Bur. Stand. 49, 409- 436, .
[3] Hoppe T., (2006) Optimization of Genetic Algorithms, Drexel University,Research Paper.
[4] Koza J. R., Bennett H. B., Andre D., & Keane M. A., (1999) Genetic programming III: Drawinian
Invention and Problem Solving, Morgan Kaufmann Publishers.
[5] Lanczos C., (1952) Solution of systems of linear equations by minimized iterations, J Res. Nat. Bur.
Standards, 49, 33-53.
[6] Michalewicz & Zbeigniew, (1996) Genetic algorithms + Data Structures = Evolution Program,
Springer – Verlog, Thirst edition.
[7] Mitchell & Melanie, (1996) An Introduction to Genetic Algorithms, Cambridge, MA:The MIT Press.
[8] Molnárka G. & Miletic, (2004) A Genetic Algorithm for Solving General System of Equations,
Department of Mathematics, Széchenyi István University, Győr, Hungary.
[9] Molnárka G. & Török B. (1996) Residual Elimination Algorithm for Solving Linear Equations and
Application to Sparse Systems, Zeitschrift für Angewandte Mathematik und Mechanik (ZAMM),
Issue 1, Numerical Analysis, Scientific Computing, Computer Science, 485-486.
[10] Saad Y. & Schultz M. H. (1986) GMRES: A generalized minimal residual algorithm for solving
nonsymmetric linear systems, SIAM J. Sci. Stat. Comput., 7, 856-869.