The use of metaheuristics is currently on the rise for solving real problems due to their complexity and uncertainty management. Most of the current existing metaheuristic algorithms have the problem of local minima a...
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The use of metaheuristics is currently on the rise for solving real problems due to their complexity and uncertainty management. Most of the current existing metaheuristic algorithms have the problem of local minima and fixed parameters. Fuzzy logic has contributed to solving this problem. It has been shown that the use of type-1 and type-2 fuzzy theory applied in parameter adaptation has contributed to effectively solve this problem. The advantage of utilizing fuzzy theory in parameter adaptation is the uncertainty management that offers a significant enhancement in finding solutions. The main goal is to utilize type-3 fuzzy theory in parameter adaptation of harmonysearch. Type-3 membership functions can use vertical slices for their construction. A new type-3 fuzzy harmonysearch approach is utilized to find the antecedent and consequent parameters of a fuzzy controller problem. Experiments with different lower scale parameters were carried out to verify the benefits of modeling the uncertainty domain with the type-3 fuzzy approach. A level of disturbance was applied to the control process to validate the performance of the method with respect to those existing in the literature.
harmony search algorithm is a good intelligent optimization algorithm, but it is not high robustness and large random. So combining variable metric method of the better local search ability, harmony search algorithm b...
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ISBN:
(纸本)9781479937066
harmony search algorithm is a good intelligent optimization algorithm, but it is not high robustness and large random. So combining variable metric method of the better local search ability, harmony search algorithm based on variable metric method is proposed. Giving the theory of the basic harmony search algorithm and variable metric method, the specific processes of the improved algorithm is analyzed, through four test functions demonstrate the performance of the improved algorithm. The numerical experiments show that the means of the algorithm are better than other given algorithms in the text, and standard deviations are better than other algorithms. This shows the improved algorithm has the better optimization ability, good robustness, and the method is easy implemented, it has a good prospect in engineering.
In recent years, there have been many multi-objective evolutionary algorithms proposed to solve multi-objective optimization problems. These evolutionary algorithms generate many solutions for iterations and move to t...
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In recent years, there have been many multi-objective evolutionary algorithms proposed to solve multi-objective optimization problems. These evolutionary algorithms generate many solutions for iterations and move to the true Pareto optimal region gradually. As expected, since the harmony search algorithm can also iterate over a large number of solutions (in HM memory) and moves to the true Pareto optimal region, we use it to solve multi-objective optimization problems. In this paper, the proposed system architecture can be divided into two phases. In the first phase, we aim to search feasible solution regions as widely as possible in the entire process. In the second phase, we focus on searching optimized solutions stepwise in the feasible solution regions. Since the proposed algorithm uses many parameters, we adjust some of them in a self-adaptive way and call the algorithm self-adaptive. In the experiments, we use the eleven well-known multi-objective problems and three many-objective problems to examine the proposed algorithm and other existing algorithms, based on five performance indicators. As a result, our algorithm achieves better performances than the others in inverted generational distance, hypervolume, and spread indicators.
Many search-based algorithms have been successfully applied in sev-eral software engineering *** algorithms(GAs)are the most used in the scientific domains by scholars to solve software testing *** imi-tate the theory ...
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Many search-based algorithms have been successfully applied in sev-eral software engineering *** algorithms(GAs)are the most used in the scientific domains by scholars to solve software testing *** imi-tate the theory of natural selection and *** harmony search algorithm(HSA)is one of the most recent searchalgorithms in the last *** imitates the behavior of a musician tofind the best *** have estimated the simi-larities and the differences between genetic algorithms and the harmony search algorithm in diverse research *** test data generation process represents a critical task in software ***,there is no work comparing the performance of genetic algorithms and the harmony search algorithm in the test data generation *** paper studies the similarities and the differences between genetic algorithms and the harmony search algorithm based on the ability and speed offinding the required test *** current research performs an empirical comparison of the HSA and the GAs,and then the significance of the results is estimated using the *** study investigates the efficiency of the harmony search algorithm and the genetic algorithms according to(1)the time performance,(2)the significance of the generated test data,and(3)the adequacy of the generated test data to satisfy a given testing *** results showed that the harmony search algorithm is significantly faster than the genetic algo-rithms because the t-Test showed that the p-value of the time values is 0.026<α(αis the significance level=0.05 at 95%confidence level).In contrast,there is no significant difference between the two algorithms in generating the adequate test data because the t-Test showed that the p-value of thefitness values is 0.25>α.
Uncertainty is a critical factor in medical datasets needed to be overcome for increasing diagnosis efficiency. This paper proposes an intelligent classification algorithm comprising a fuzzy rule-based approach, a har...
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Uncertainty is a critical factor in medical datasets needed to be overcome for increasing diagnosis efficiency. This paper proposes an intelligent classification algorithm comprising a fuzzy rule-based approach, a harmonysearch (HS) algorithm, and a heuristic algorithm to classify medical datasets intelligently. Two fuzzy approaches, as well as orthogonal and triangular fuzzy sets, are first utilized to define the attributes of data. Then, an HS algorithm is integrated with a heuristic to generate fuzzy rules to select the best rules in the fuzzy rule-based systems. Moreover, to improve the performance of the proposed classification approach, a three-phase parameter tuning approach is applied. First, the Taguchi method (phase I) is employed to tune the parameters of the HS algorithm using a fixed number of training data and find the central points of the parameters' values. Then, a nested cross-validation (CV) approach consisting of an outer CV (phase II) and an inner CV (phase III) is utilized. Using the Taguchi approach gives the advantage of not considering a wide range of parameters by the nested CV which produces better results on the medical dataset. Nine well-known medical datasets are used to evaluate the efficiency of the proposed hybrid algorithm. To this aim, the results obtained by the algorithm are compared with the ones of several related works in the literature where several statistical tests and graphical approaches are used for comparisons. The results show that the proposed methods are robust in data analysis and classification of clinical datasets. (c) 2021 Published by Elsevier B.V.
Today, automated extractive text summarization is one of the most common techniques for organizing information. In extractive summarization, the most appropriate sentences are selected from the text and build a repres...
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Today, automated extractive text summarization is one of the most common techniques for organizing information. In extractive summarization, the most appropriate sentences are selected from the text and build a representative summary. Therefore, probing for the best sentences is a fundamental task. This paper has coped with extractive summarization as a multi-objective optimization problem and proposed a language-independent, semantic-aware approach that applies the harmony search algorithm to generate appropriate multi-document summaries. It learns the objective function from an extra set of reference summaries and then generates the best summaries according to the trained function. The system also performs some supplementary activities for better achievements. It expands the sentences by using an inventive approach that aims at tuning conceptual densities in the sentences towards important topics. Furthermore, we introduced an innovative clustering method for identifying important topics and reducing redundancies. A sentence placement policy based on the Hamiltonian shortest path was introduced for producing readable summaries. The experiments were conducted on DUC2002, DUC2006 and DUC2007 datasets. Experimental results showed that the proposed framework could assist the summarization process and yield better performance. Also, it was able to generally outperform other cited summarizer systems.
Many optimisation problems are dynamic in the sense that changes occur during the optimisation process, and therefore are more challenging than the stationary problems. To solve dynamic optimisation problems, the prop...
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Many optimisation problems are dynamic in the sense that changes occur during the optimisation process, and therefore are more challenging than the stationary problems. To solve dynamic optimisation problems, the proposed approaches should not only attempt to seek the global optima but be able to also keep track of changes in the track record of landscape solutions. In this research work, one of the most recent new population-based meta-heuristic optimisation technique called a harmony search algorithm for dynamic optimization problems is investigated. This technique mimics the musical process when a musician attempts to find a state of harmony. In order to cope with a dynamic behaviour, the proposed harmony search algorithm was hybridised with a (i) random immigrant, (ii) memory mechanism and (iii) memory based immigrant scheme. The performance of the proposed harmonysearch is verified by using the well-known dynamic test problem called the Moving Peak Benchmark (MPB) with a variety of peaks. The empirical results demonstrate that the proposed algorithm is able to obtain competitive results, but not the best for most of the cases, when compared to the best known results in the scientific literature published so far.
harmony search algorithm with multi-parent crossover (HSA-MPC) is a hybrid algorithm that relies on benefiting from the crossover operation to combine more than one harmony to generate a new harmony. The picked harmon...
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ISBN:
(纸本)9783030295165;9783030295158
harmony search algorithm with multi-parent crossover (HSA-MPC) is a hybrid algorithm that relies on benefiting from the crossover operation to combine more than one harmony to generate a new harmony. The picked harmonies are taken from an archive pool with best harmonies. In a previous study, the algorithm proves its efficiency when compared to other harmony search algorithms. In this paper, we will study the effect of harmony memory size (HMS), harmony memory consideration rate (HMCR), multi-parent crossover rate (MPCR), and the archive pool size on the quality of the generated solution. Eleven different scenarios are evaluated using a set of eight real-world numerical optimization problems introduced for CEC 2011 evolutionary algorithm competition. The analysis provides fixed values for all operators except the one under investigation. The obtained results prove the sensitivity of the algorithm to these operators and suggest a set of recommendations to improve the algorithm performance.
The method of least absolute deviation (LAD) finds applications in many areas, due to its robustness compared to the least squares regression (LSR) method. LAD is robust in that it is resistant to outliers in the data...
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The method of least absolute deviation (LAD) finds applications in many areas, due to its robustness compared to the least squares regression (LSR) method. LAD is robust in that it is resistant to outliers in the data. This may be helpful in studies where outliers may be ignored. Since LAD is nonsmooth optimization problem, this paper proposed a metaheuristics algorithm named novel global harmonysearch (NGHS) for solving. Numerical results show that the NGHS method has good convergence property and effective in solving LAD.
This paper reports a novel image thresholding method based on fuzzy set theory and maximum correlation criterion using harmony search algorithm. In this study, the maximum fuzzy correlation criterion is defined using ...
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This paper reports a novel image thresholding method based on fuzzy set theory and maximum correlation criterion using harmony search algorithm. In this study, the maximum fuzzy correlation criterion is defined using Z- and S-fuzzy member function on image gray level histogram. Then fuzzy correlation criterion image segmentation based on harmony search algorithm is implemented. The experimental studies were conducted on a variety of images by testing the proposed method and some classical thresholding methods. The experimental results demonstrate that the proposed method can select the threshold automatically and effectively. Compared with the exhaustive search method, the harmony search algorithm can give high degree of accuracy while needing less search time and has good search stability in the segmentation experiments.
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