This article proposes a novel binary version of recently developed gaining sharing knowledge-based optimization algorithm (GSK) to solve binary optimization problems. GSK algorithm is based on the concept of how human...
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This article proposes a novel binary version of recently developed gaining sharing knowledge-based optimization algorithm (GSK) to solve binary optimization problems. GSK algorithm is based on the concept of how humans acquire and share knowledge during their life span. A binary version of GSK named novel binary gaining sharing knowledge-based optimization algorithm (NBGSK) depends on mainly two binary stages: binary junior gainingsharing stage and binary senior gainingsharing stage with knowledge factor 1. These two stages enable NBGSK for exploring and exploitation of the search space efficiently and effectively to solve problems in binary space. Moreover, to enhance the performance of NBGSK and prevent the solutions from trapping into local optima, NBGSK with population size reduction (PR-NBGSK) is introduced. It decreases the population size gradually with a linear function. The proposed NBGSK and PR-NBGSK applied to set of knapsack instances with small and large dimensions, which shows that NBGSK and PR-NBGSK are more efficient and effective in terms of convergence, robustness, and accuracy.
The gainingsharingknowledgebasedoptimizationalgorithm (GSK) is recently developed metaheuristic algorithm, which is based on how humans acquire and share knowledge during their life-time. This paper investigates ...
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The gainingsharingknowledgebasedoptimizationalgorithm (GSK) is recently developed metaheuristic algorithm, which is based on how humans acquire and share knowledge during their life-time. This paper investigates a modified version of the GSK algorithm to find the best feature subsets. Firstly, it represents a binary variant of GSK algorithm by employing a probability estimation operator (Bi-GSK) on the two main pillars of GSK algorithm. And then, the chaotic maps are used to enhance the performance of the proposed algorithm. Ten different types of chaotic maps are considered to adapt the parameters of the GSK algorithm that make a proper balance between exploration and exploitation and save the algorithm from premature convergence. To check the performance of proposed approaches of GSK algorithm, twenty-one benchmark datasets are taken from the UCI repository for feature selection. The performance is measured by calculating different type of measures, and several metaheuristic algorithms are adopted to compare the obtained results. The results indicate that Chebyshev chaotic map shows the best result among all chaotic maps which improve the performance accuracy and convergence rate of the original algorithm. Moreover, it outperforms the other metaheuristic algorithms in terms of efficiency, fitness value and the minimum number of selected features.
This article proposes a novel binary version of recently developed gaining-sharingknowledge-basedoptimizationalgorithm (GSK) to solve binary optimization problems. GSK algorithm is based on the concept of how human...
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This article proposes a novel binary version of recently developed gaining-sharingknowledge-basedoptimizationalgorithm (GSK) to solve binary optimization problems. GSK algorithm is based on the concept of how humans acquire and share knowledge during their life span. A discrete binary version of GSK named novel binary gaining sharing knowledge-based optimization algorithm (DBGSK) depends on mainly two binary stages: binary junior gainingsharing stage and binary senior gainingsharing stage with knowledge factor 1. These two stages enable DBGSK for exploring and exploitation of the search space efficiently and effectively to solve problems in binary space. An improved scheduling of the technical counselling process for utilization of the electricity from solar energy power stations is introduced. The scheduling aims at achieving the best utilization of the available day time for the counselling group, and a new application problem is presented, which is called a travelling counselling problem (TCP). A nonlinear binary model is introduced with a real application.
Noise reduction is one of the main challenges for researchers. Classical image de-noising methods reduce the image noise but sometimes lose image quality and information, such as blurring the edges of the image. To so...
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Noise reduction is one of the main challenges for researchers. Classical image de-noising methods reduce the image noise but sometimes lose image quality and information, such as blurring the edges of the image. To solve this challenge, this work proposes two optimal filters based on a generalized Cauchy (GC) distribution and two different nature-inspired algorithms that preserve image information while decreasing the noise. The generalized Cauchy filter and the bilateral filter are two parameter-based filters that significantly remove image noise. Parameter-based filters require proper parameter selection to remove the noise and maintain the edge details. To this end, two filters are considered. In the previous works, the parameters of the mask that was made with the GC function were optimized and the mask size was considered fixed. By studying different noisy images, we find that the selected mask size significantly impacts the designed filter performance. Therefore in this paper, a mask is designed using the GC function to formulate the first filter, and despite the optimization of the filter parameters, the selected mask size is also optimized using the peak signal-to-noise ratio (PSNR) as a fitness function. In most metaheuristic-based bilateral filters, only the domain and range parameters, which are based on Gaussian distribution, are optimized and the neighboring radius is a constant value. Filter results on different noisy images show that the neighboring radius has a major effect on the filter performance. Since the filter designed with the GC function causes significant noise removal, this function is effective, and on the other hand, it’s almost similar behavior with the Gaussian function has caused it to be combined with the bilateral filter to design the second filter in this paper. The kernel of the domain and range is considered to be the GC function instead of the Gaussian function. The domain and range parameters and the neighboring radius are opti
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