Pattern Analysis and Intelligent Systems
Neda Damya; Farhad Soleimanian Gharehchopogh
Volume 6, Issue 4 , November 2020, , Pages 227-238
Abstract
Clustering is a method of data analysis and one of the important methods in data mining that has been considered by researchers in many fields as well as in many disciplines. In this paper, we propose combining WOA with BA for data clustering. To assess the efficiency of the proposed method, it has been ...
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Clustering is a method of data analysis and one of the important methods in data mining that has been considered by researchers in many fields as well as in many disciplines. In this paper, we propose combining WOA with BA for data clustering. To assess the efficiency of the proposed method, it has been applied in data clustering. In the proposed method, first, by examining BA thoroughly, the weaknesses of this algorithm in exploitation and exploration are identified. The proposed method focuses on improving BA exploitation. Therefore, in the proposed method, instead of the random selection step, one solution is selected from the best solutions, and some of the dimensions of the position vector in BA are replaced We change some of the best solutions with the step of reducing the encircled mechanism and updating the WOA spiral, and finally, after selecting the best exploitation between the two stages of WOA exploitation and BA exploitation, the desired changes are applied on solutions. We evaluate the performance of the proposed method in comparison with other meta-heuristic algorithms in the data clustering discussion using six datasets. The results of these experiments show that the proposed method is statistically much better than the standard BA and also the proposed method is better than the WOA. Overall, the proposed method was more robust and better than the Harmony Search Algorithm (HAS), Artificial Bee Colony (ABC), WOA and BA.
Pattern Analysis and Intelligent Systems
narges jafari; Farhad Soleimanian Gharehchopogh
Volume 6, Issue 3 , August 2020, , Pages 119-132
Abstract
Metaheuristic algorithms are used to solve NP-hard optimization problems. These algorithms have two main components, i.e. exploration and exploitation, and try to strike a balance between exploration and exploitation to achieve the best possible near-optimal solution. The bat algorithm is one of the ...
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Metaheuristic algorithms are used to solve NP-hard optimization problems. These algorithms have two main components, i.e. exploration and exploitation, and try to strike a balance between exploration and exploitation to achieve the best possible near-optimal solution. The bat algorithm is one of the metaheuristic algorithms with poor exploration and exploitation. In this paper, exploration and exploitation processes of Gray Wolf Optimizer (GWO) algorithm are applied to some of the solutions produced by the bat algorithm. Therefore, part of the population of the bat algorithm is changed by two processes (i.e. exploration and exploitation) of GWO; the new population enters the bat algorithm population when its result is better than that of the exploitation and exploration operators of the bat algorithm. Thereby, better new solutions are introduced into the bat algorithm at each step. In this paper, 20 mathematic benchmark functions are used to evaluate and compare the proposed method. The simulation results show that the proposed method outperforms the bat algorithm and other metaheuristic algorithms in most implementations and has a high performance.
Software Engineering and Information Systems
Ramin Saljoughinejad; Vahid Khatibi
Volume 4, Issue 1 , February 2018, , Pages 27-40
Abstract
The literature review shows software development projects often neither meet time deadlines, nor run within the allocated budgets. One common reason can be the inaccurate cost estimation process, although several approaches have been proposed in this field. Recent research studies suggest that in order ...
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The literature review shows software development projects often neither meet time deadlines, nor run within the allocated budgets. One common reason can be the inaccurate cost estimation process, although several approaches have been proposed in this field. Recent research studies suggest that in order to increase the accuracy of this process, estimation models have to be revised. The Constructive Cost Model (COCOMO) has often been referred as an efficient model for software cost estimation. The popularity of COCOMO is due to its flexibility; it can be used in different environments and it covers a variety of factors. In this paper, we aim to improve the accuracy of cost estimation process by enhancing COCOMO model. To this end, we analyze the cost drivers using meta-heuristic algorithms. In this method, the improvement of COCOMO is distinctly done by effective selection of coefficients and reconstruction of COCOMO. Three meta-heuristic optimization algorithms are applied synthetically to enhance the process of COCOMO model. Eventually, results of the proposed method are compared to COCOMO itself and other existing models. This comparison explicitly reveals the superiority of the proposed method.