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Enhanced harmony search for MPPT control in solar systems

RESULTS IN ENGINEERING 2025年 25卷

作者： Lu, Hao-Ying Ye, Cheng-En Tsai, Hao-Yu Huang, Yu-Pei Natl Quemoy Univ Dept Elect Engn Jinning 892 Kimen Taiwan Natl Chin Yi Univ Technol Dept Refrigerat Air Condit & Energy Engn Taichung 411 Taiwan

This study introduces a novel approach to optimize the harmony search algorithm (HSA) for maximum power point tracking (MPPT) in solar systems under partial shading conditions (PSCs). The enhanced HSA incorporates a Normal Distribution Approach (NDA) to refine the search process and a Simplified Sampling Process (SSP) to efficiently calculate nearby sampling points using interpolation. Experimental results demonstrate that the proposed algorithm significantly reduces the number of sampling points (by approximately 62.95 %) while maintaining comparable accuracy (around 94.5 %). The algorithm offers the benefits of high energy efficiency and effective tracking of maximum power under partial shading conditions. The optimized parameter settings provide valuable insights for future research in this field.

关键词： Photovoltaic array Partial shading condition Maximum power point tracking harmony search algorithm Simplified sampling process

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A Type-3 Fuzzy Parameter Adjustment in harmony search for the Parameterization of Fuzzy Controllers

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INTERNATIONAL JOURNAL OF FUZZY SYSTEMS 2023年第6期25卷 2281-2294页

作者： Peraza, Cinthia Castillo, Oscar Melin, Patricia Castro, Juan R. Yoon, Jin Hee Geem, Zong Woo Tijuana Inst Technol Div Grad Studies & Res Tijuana 22379 Mexico UABC Univ Tijuana Mexico Sejong Univ Sch Math & Stat Seoul South Korea Gachon Univ Coll IT Convergence Seongnam South Korea

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 harmony search. Type-3 membership functions can use vertical slices for their construction. A new type-3 fuzzy harmony search 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.

关键词： Type-3 fuzzy logic Fuzzy controller Parameterization harmony search algorithm

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Research of harmony search algorithm based on variable metric method

Research of Harmony Search Algorithm based on variable metri...

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26th Chinese Control and Decision Conference (CCDC)

作者： Qi Wei Liu Xuwang Henan Univ Sch Business Adm Kaifeng 475004 Peoples R China

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.

关键词： intelligent algorithms variable metric method harmony search algorithm robustness

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Solving multi-objective optimization problems using self-adaptive harmony search algorithms

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SOFT COMPUTING 2020年第6期24卷 4081-4107页

作者： Huang, Yin-Fu Chen, Sih-Hao Natl Yunlin Univ Sci & Technol Dept Comp Sci & Informat Engn 123 Univ RdSection 3 Touliu 640 Yunlin Taiwan

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.

关键词： harmony search algorithm Optimization method Pareto optimization Multi-objective optimization Self-adaptive

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Generating of Test Data by harmony search Against Genetic algorithms

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Intelligent Automation & Soft Computing 2023年第4期36卷 647-665页

作者： Ahmed S.Ghiduk Abdullah Alharbi Department of Information Technology College of Computers and Information TechnologyTaif UniversityP.O.Box 11099Taif21944Saudi Arabia Department of Mathematics and Computer Science Faculty of ScienceBeni-Suef UniversityBeni-Suef62521Egypt

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 search algorithms 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.

关键词： harmony search algorithm genetic algorithms test data generation

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An intelligent hybrid classification algorithm integrating fuzzy rule-based extraction and harmony search optimization: Medical diagnosis applications

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KNOWLEDGE-BASED SYSTEMS 2021年 220卷 106943-106943页

作者： Mousavi, Seyed Mohsen Abdullah, Salwani Niaki, Seyed Taghi Akhavan Banihashemi, Saeed Sharif Univ Technol Dept Ind Engn POB 11155-9414Azadi Ave Tehran *** Iran Univ Kebangsaan Malaysia Fac Informat Sci & Technol Ctr Artificial Intelligence Technol Data Min & Optimizat Res Grp DMO Bangi 43600 Selangor Malaysia Univ Canberra Design & Built Environm Sch Canberra ACT Australia

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 harmony search (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.

关键词： Machine learning classifier Fuzzy rule-based system harmony search algorithm Taguchi method Nested cross-validation Medical diagnosis

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Text Summarization as a Multi-objective Optimization Task: Applying harmony search to Extractive Multi-Document Summarization

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COMPUTER JOURNAL 2022年第5期65卷 1053-1072页

作者： Bidoki, M. Fakhrahmad, M. Moosavi, M. R. Shiraz Univ Sch Elect & Comp Engn Dept Comp Sci & Engn Shiraz *** Iran

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.

关键词： multi-document extractive summarization optimization problem harmony search algorithm sentence expansion conceptual density tuning language-independent approach word embedding graph-based ranking objective function learning text clustering

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A Hybrid harmony search algorithm for Solving Dynamic Optimisation Problems

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Procedia Computer Science 2014年 29卷 1926-1936页

作者： Ayad Mashaan Turky Salwani Abdullah Nasser R. Sabar Universiti Kebangsaan Malaysia 43600 Bangi Selangor Malaysia Faculty of Information & Communication Technologies Swinburne University of Technology Victoria 3122 Australia The University of Nottingham Malaysia Campus Jalan Broga 43500 Semenyih Selangor Malaysia

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 harmony search 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.

关键词： harmony search algorithm Dynamic optimization problems Meta-Heuristic

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A Sensitivity Analysis for harmony search with Multi-Parent Crossover algorithm

A Sensitivity Analysis for Harmony Search with Multi-Parent ...

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Intelligent Systems Conference (IntelliSys)

作者： Abu Doush, Iyad Santos, Eugene Amer Univ Kuwait Comp Sci & Informat Syst Dept Salmiya Kuwait Dartmouth Coll Thayer Engn Sch 8000 Cummings Hall Hanover NH 03755 USA

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.

关键词： harmony search algorithm Evolutionary algorithms Numerical optimization Hybrid harmony search algorithm

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Novel Global harmony search algorithm for Least Absolute Deviation

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JOURNAL OF APPLIED MATHEMATICS 2014年第Pt.7期2014卷 632975-1-632975-6页

作者： Yong, Longquan Shaanxi Univ Technol Sch Math & Comp Sci Hanzhong 723001 Peoples R China

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 harmony search (NGHS) for solving. Numerical results show that the NGHS method has good convergence property and effective in solving LAD.

关键词： Novel Global harmony search algorithm Least Absolute Deviation searchING algorithm absolute deviation outliers Robustness

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