adugna fita | Adama Science and Technology University (original) (raw)
Papers by adugna fita
American Journal of Computational and Applied Mathematics, 2015
Due to the complexity of many real-world optimization problems, better optimization algorithms ar... more Due to the complexity of many real-world optimization problems, better optimization algorithms are always needed. Complex optimization problems that cannot be solved using classical approaches require efficient search metaheuristics to find optimal solutions. Recently, metaheuristic global optimization algorithms becomes a popular choice and more practical for solving complex and loosely defined problems, which are otherwise difficult to solve by traditional methods. This is due to their nature that implies discontinuities of the search space, non differentiability of the objective functions and initial feasible solutions. But metaheuristic global optimization algorithms are less susceptible to discontinuity and differentiability and also bad proposals of initial feasible solution do not affect the end solution. Thus, an initial feasible solution gauss for gradient based optimization algorithms can be generated with well known population based metaheuristic Genetic Algorithm. The continuous genetic algorithm will easily couple to gradient based optimization, since gradient based optimizers use continuous variables. Therefore, Instead of starting with initial guess, random starting with genetic algorithm finds the region of the optimum value, and then gradient based optimizer takes over to find the global optimum. In this paper the hybrid of metaheuristic global search, followed with gradient based optimization methods shows great improvements on optimal solution than using separately.
International Journal of Scientific & Technology Research, Sep 25, 2014
Nowadays, we want to have a good life, which may mean more wealth, more power, more respect and m... more Nowadays, we want to have a good life, which may mean more wealth, more power, more respect and more time for our selves, together with a good health and a good second generation, etc. Indeed, all important political, economical and cultural events have involved multiple criteria in their evolution. Multiobjective optimization deals with the investigation of optimization problems that possess more than one objective function. Usually there is no single solution that optimizes all functions simultaneously, we have solution set that is called nondominated set and elements of this set are usually infinite. It is from this set decision is made by taking elements of nondominated set as alternatives, which is given by analysts. But practically extraction of nondominated solutions and setting fitness function are difficult. This Paper will try to solve problems that the decision maker face in extraction of Pareto optimal solution with continuous variable genetic algorithm and taking objective function as fitness function without modification by considering box constraint and generating initial solution within box constraint and penalty function for constrained one. Solutions will be kept in feasible region during mutation and recombination.
One of the challenges in optimizing clustering problems is requirement of differentiability. Clus... more One of the challenges in optimizing clustering problems is requirement of differentiability. Clustering is a popular approach that classifies a given data into different groups , based on some common properties. It is a basic foundation of machine learning , facility location and image processing. Although the problem is nonsmooth, we used Nesterov partial smoothing technique to approximate nondifferentiable convex functions by smooth convex functions with Lipschitz continuous gradients. In this paper, we mainly focused in modelling and solving clustering problems that identify some nodes as cluster centers among others and minimize the
overall L1 distance of the clusters. In addition, since the algorithm starts with any initial cluster centers penalty parameter is used to push centers to real node. As a result, a DCA based
algorithms were implemented that find optimal cluster centers in reasonable iteration time.
Applied Mathematics, 2014
Due to the complexity of many real-world optimization problems, better optimization algorithms ar... more Due to the complexity of many real-world optimization problems, better optimization algorithms are always needed. Complex optimization problems that cannot be solved using classical approaches require efficient search metaheuristics to find optimal solutions. Recently, metaheuristic global optimization algorithms becomes a popular choice and more practical for solving complex and loosely defined problems, which are otherwise difficult to solve by traditional methods. This is due to their nature that implies discontinuities of the search space, non differentiability of the objective functions and initial feasible solutions. But metaheuristic global optimization algorithms are less susceptible to discontinuity and differentiability and also bad proposals of initial feasible solution do not affect the end solution. Thus, an initial feasible solution gauss for gradient based optimization algorithms can be generated with well known population based metaheuristic Genetic Algorithm. The co...
Nowadays, we want to have a good life, which may mean more wealth, more power, more respect and m... more Nowadays, we want to have a good life, which may mean more wealth, more power, more respect and more time for our selves, together with a good health and a good second generation, etc. Indeed, all important political, economical and cultural events have involved multiple criteria in their evolution. Multiobjective optimization deals with the investigation of optimization problems that possess more than one objective function. Usually there is no single solution that optimizes all functions simultaneously, we have solution set that is called nondominated set and elements of this set are usually infinite. It is from this set decision is made by taking elements of nondominated set as alternatives, which is given by analysts. But practically extraction of nondominated solutions and setting fitness function are difficult. This Paper will try to solve problems that the decision maker face in extraction of Pareto optimal solution with continuous variable genetic algorithm and taking object...
Journal of Mathematical and Statistical AnalysisIntroduction, 2019
Digital watermarking is the process of embedding information into a digital signal such as image,... more Digital watermarking is the process of embedding information into a digital signal such as image, video, audio data to easily identify the ownership of the original data. Such information is embedded for many different purposes, such as copyright protection, source tracking, piracy deterrence, tamperproof etc. Therefore, it shall be embedded in a way that makes it difficult to be visualize with human eye and difficult to be removed. As computers are more and more integrated via the network, the distribution of digital data is becoming faster, easier, and requiring less effort to make exact copies. One of the current research areas is to protect digital watermark inside the information so that ownership of the information cannot be claimed by third party. In this paper, we propose an algorithm for colored digital image watermarking technique based on singular value decomposition. This paper covers embedding, watermark extraction algorithm and some robustness tests while both host and watermark images are colored. The quality of the watermarked image is tested through experiment against most common attacks such as image compression, filtering, cropping, injection of noise, blurring, and sharpening. Standard benchmark was used to test the robustness of the proposed watermarking algorithm. Experimental result shows that the algorithm is robust against geometric attacks.
Due to the complexity of many real-world optimization problems, better optimization algorithms ar... more Due to the complexity of many real-world optimization problems, better optimization algorithms are always needed. Complex optimization problems that cannot be solved using classical approaches require efficient search metaheuristics to find optimal solutions. Recently, metaheuristic global optimization algorithms becomes a popular choice and more practical for solving complex and loosely defined problems, which are otherwise difficult to solve by traditional methods. This is due to their nature that implies discontinuities of the search space, non differentiability of the objective functions and initial feasible solutions. But metaheuristic global optimization algorithms are less susceptible to discontinuity and differentiability and also bad proposals of initial feasible solution do not affect the end solution. Thus, an initial feasible solution gauss for gradient based optimization algorithms can be generated with well known population based metaheuristic Genetic Algorithm. The continuous genetic algorithm will easily couple to gradient based optimization, since gradient based optimizers use continuous variables. Therefore, Instead of starting with initial guess, random starting with genetic algorithm finds the region of the optimum value, and then gradient based optimizer takes over to find the global optimum. In this paper the hybrid of metaheuristic global search, followed with gradient based optimization methods shows great improvements on optimal solution than using separately.
The subject area of multiobjective optimization deals with the investigation of optimization prob... more The subject area of multiobjective optimization deals with the investigation of optimization problems
that possess more than one objective function. Usually, there does not exist a single solution
that optimizes all functions simultaneously; quite the contrary, we have solution set that is called
nondominated set and elements of this set are usually infinite. It is from this set decision made by
taking elements of nondominated set as alternatives, which is given by analysts. Since it is important
for the decision maker to obtain as much information as possible about this set, our research
objective is to determine a well-defined and meaningful approximation of the solution set for linear
and nonlinear three objective optimization problems. In this paper a continuous variable
genetic algorithm is used to find approximate near optimal solution set. Objective functions are
considered as fitness function without modification. Initial solution was generated within box
constraint and solutions will be kept in feasible region during mutation and recombination.
Nowadays, we want to have a good life, which may mean more wealth, more power, more respect and m... more Nowadays, we want to have a good life, which may mean more wealth, more power, more respect and more time for our selves, together with a good health and a good second generation, etc. Indeed, all important political, economical and cultural events have involved multiple criteria in their evolution. Multiobjective optimization deals with the investigation of optimization problems that possess more than one objective function. Usually there is no single solution that optimizes all functions simultaneously, we have solution set that is called nondominated set and elements of this set are usually infinite. It is from this set decision is made by taking elements of nondominated set as alternatives, which is given by analysts. But practically extraction of nondominated solutions and setting fitness function are difficult. This Paper will try to solve problems that the decision maker face in extraction of Pareto optimal solution with continuous variable genetic algorithm and taking objective function as fitness function without modification by considering box constraint and generating initial solution within box constraint and penalty function for constrained one. Solutions will be kept in feasible region during mutation and recombination
American Journal of Computational and Applied Mathematics, 2015
Due to the complexity of many real-world optimization problems, better optimization algorithms ar... more Due to the complexity of many real-world optimization problems, better optimization algorithms are always needed. Complex optimization problems that cannot be solved using classical approaches require efficient search metaheuristics to find optimal solutions. Recently, metaheuristic global optimization algorithms becomes a popular choice and more practical for solving complex and loosely defined problems, which are otherwise difficult to solve by traditional methods. This is due to their nature that implies discontinuities of the search space, non differentiability of the objective functions and initial feasible solutions. But metaheuristic global optimization algorithms are less susceptible to discontinuity and differentiability and also bad proposals of initial feasible solution do not affect the end solution. Thus, an initial feasible solution gauss for gradient based optimization algorithms can be generated with well known population based metaheuristic Genetic Algorithm. The continuous genetic algorithm will easily couple to gradient based optimization, since gradient based optimizers use continuous variables. Therefore, Instead of starting with initial guess, random starting with genetic algorithm finds the region of the optimum value, and then gradient based optimizer takes over to find the global optimum. In this paper the hybrid of metaheuristic global search, followed with gradient based optimization methods shows great improvements on optimal solution than using separately.
International Journal of Scientific & Technology Research, Sep 25, 2014
Nowadays, we want to have a good life, which may mean more wealth, more power, more respect and m... more Nowadays, we want to have a good life, which may mean more wealth, more power, more respect and more time for our selves, together with a good health and a good second generation, etc. Indeed, all important political, economical and cultural events have involved multiple criteria in their evolution. Multiobjective optimization deals with the investigation of optimization problems that possess more than one objective function. Usually there is no single solution that optimizes all functions simultaneously, we have solution set that is called nondominated set and elements of this set are usually infinite. It is from this set decision is made by taking elements of nondominated set as alternatives, which is given by analysts. But practically extraction of nondominated solutions and setting fitness function are difficult. This Paper will try to solve problems that the decision maker face in extraction of Pareto optimal solution with continuous variable genetic algorithm and taking objective function as fitness function without modification by considering box constraint and generating initial solution within box constraint and penalty function for constrained one. Solutions will be kept in feasible region during mutation and recombination.
One of the challenges in optimizing clustering problems is requirement of differentiability. Clus... more One of the challenges in optimizing clustering problems is requirement of differentiability. Clustering is a popular approach that classifies a given data into different groups , based on some common properties. It is a basic foundation of machine learning , facility location and image processing. Although the problem is nonsmooth, we used Nesterov partial smoothing technique to approximate nondifferentiable convex functions by smooth convex functions with Lipschitz continuous gradients. In this paper, we mainly focused in modelling and solving clustering problems that identify some nodes as cluster centers among others and minimize the
overall L1 distance of the clusters. In addition, since the algorithm starts with any initial cluster centers penalty parameter is used to push centers to real node. As a result, a DCA based
algorithms were implemented that find optimal cluster centers in reasonable iteration time.
Applied Mathematics, 2014
Due to the complexity of many real-world optimization problems, better optimization algorithms ar... more Due to the complexity of many real-world optimization problems, better optimization algorithms are always needed. Complex optimization problems that cannot be solved using classical approaches require efficient search metaheuristics to find optimal solutions. Recently, metaheuristic global optimization algorithms becomes a popular choice and more practical for solving complex and loosely defined problems, which are otherwise difficult to solve by traditional methods. This is due to their nature that implies discontinuities of the search space, non differentiability of the objective functions and initial feasible solutions. But metaheuristic global optimization algorithms are less susceptible to discontinuity and differentiability and also bad proposals of initial feasible solution do not affect the end solution. Thus, an initial feasible solution gauss for gradient based optimization algorithms can be generated with well known population based metaheuristic Genetic Algorithm. The co...
Nowadays, we want to have a good life, which may mean more wealth, more power, more respect and m... more Nowadays, we want to have a good life, which may mean more wealth, more power, more respect and more time for our selves, together with a good health and a good second generation, etc. Indeed, all important political, economical and cultural events have involved multiple criteria in their evolution. Multiobjective optimization deals with the investigation of optimization problems that possess more than one objective function. Usually there is no single solution that optimizes all functions simultaneously, we have solution set that is called nondominated set and elements of this set are usually infinite. It is from this set decision is made by taking elements of nondominated set as alternatives, which is given by analysts. But practically extraction of nondominated solutions and setting fitness function are difficult. This Paper will try to solve problems that the decision maker face in extraction of Pareto optimal solution with continuous variable genetic algorithm and taking object...
Journal of Mathematical and Statistical AnalysisIntroduction, 2019
Digital watermarking is the process of embedding information into a digital signal such as image,... more Digital watermarking is the process of embedding information into a digital signal such as image, video, audio data to easily identify the ownership of the original data. Such information is embedded for many different purposes, such as copyright protection, source tracking, piracy deterrence, tamperproof etc. Therefore, it shall be embedded in a way that makes it difficult to be visualize with human eye and difficult to be removed. As computers are more and more integrated via the network, the distribution of digital data is becoming faster, easier, and requiring less effort to make exact copies. One of the current research areas is to protect digital watermark inside the information so that ownership of the information cannot be claimed by third party. In this paper, we propose an algorithm for colored digital image watermarking technique based on singular value decomposition. This paper covers embedding, watermark extraction algorithm and some robustness tests while both host and watermark images are colored. The quality of the watermarked image is tested through experiment against most common attacks such as image compression, filtering, cropping, injection of noise, blurring, and sharpening. Standard benchmark was used to test the robustness of the proposed watermarking algorithm. Experimental result shows that the algorithm is robust against geometric attacks.
Due to the complexity of many real-world optimization problems, better optimization algorithms ar... more Due to the complexity of many real-world optimization problems, better optimization algorithms are always needed. Complex optimization problems that cannot be solved using classical approaches require efficient search metaheuristics to find optimal solutions. Recently, metaheuristic global optimization algorithms becomes a popular choice and more practical for solving complex and loosely defined problems, which are otherwise difficult to solve by traditional methods. This is due to their nature that implies discontinuities of the search space, non differentiability of the objective functions and initial feasible solutions. But metaheuristic global optimization algorithms are less susceptible to discontinuity and differentiability and also bad proposals of initial feasible solution do not affect the end solution. Thus, an initial feasible solution gauss for gradient based optimization algorithms can be generated with well known population based metaheuristic Genetic Algorithm. The continuous genetic algorithm will easily couple to gradient based optimization, since gradient based optimizers use continuous variables. Therefore, Instead of starting with initial guess, random starting with genetic algorithm finds the region of the optimum value, and then gradient based optimizer takes over to find the global optimum. In this paper the hybrid of metaheuristic global search, followed with gradient based optimization methods shows great improvements on optimal solution than using separately.
The subject area of multiobjective optimization deals with the investigation of optimization prob... more The subject area of multiobjective optimization deals with the investigation of optimization problems
that possess more than one objective function. Usually, there does not exist a single solution
that optimizes all functions simultaneously; quite the contrary, we have solution set that is called
nondominated set and elements of this set are usually infinite. It is from this set decision made by
taking elements of nondominated set as alternatives, which is given by analysts. Since it is important
for the decision maker to obtain as much information as possible about this set, our research
objective is to determine a well-defined and meaningful approximation of the solution set for linear
and nonlinear three objective optimization problems. In this paper a continuous variable
genetic algorithm is used to find approximate near optimal solution set. Objective functions are
considered as fitness function without modification. Initial solution was generated within box
constraint and solutions will be kept in feasible region during mutation and recombination.
Nowadays, we want to have a good life, which may mean more wealth, more power, more respect and m... more Nowadays, we want to have a good life, which may mean more wealth, more power, more respect and more time for our selves, together with a good health and a good second generation, etc. Indeed, all important political, economical and cultural events have involved multiple criteria in their evolution. Multiobjective optimization deals with the investigation of optimization problems that possess more than one objective function. Usually there is no single solution that optimizes all functions simultaneously, we have solution set that is called nondominated set and elements of this set are usually infinite. It is from this set decision is made by taking elements of nondominated set as alternatives, which is given by analysts. But practically extraction of nondominated solutions and setting fitness function are difficult. This Paper will try to solve problems that the decision maker face in extraction of Pareto optimal solution with continuous variable genetic algorithm and taking objective function as fitness function without modification by considering box constraint and generating initial solution within box constraint and penalty function for constrained one. Solutions will be kept in feasible region during mutation and recombination