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moth search algorithm: a bio-inspired metaheuristic algorithm for global optimization problems

MEMETIC COMPUTING 2018年第2期10卷 151-164页

作者： Wang, Gai-Ge Jiangsu Normal Univ Sch Comp Sci & Technol Xuzhou 221116 Jiangsu Peoples R China Univ Alberta Dept Elect & Comp Engn Edmonton AB T6R 2V4 Canada Northeast Normal Univ Inst Algorithm & Big Data Anal Changchun 130117 Jilin Peoples R China Northeast Normal Univ Sch Comp Sci & Informat Technol Changchun 130117 Jilin Peoples R China

Phototaxis, signifying movement of an organism towards or away from a source of light, is one of the most representative features for moths. It has recently been shown that one of the characteristics of moths has been the propensity to follow Levy flights. Inspired by the phototaxis and Levy flights of the moths, a new kind of metaheuristic algorithm, called moth search (MS) algorithm, is developed in the present work. In nature, moths are a family insects associated with butterflies belonging to the order Lepidoptera. In MS method, the best moth individual is viewed as the light source. Some moths that are close to the fittest one always display an inclination to fly around their own positions in the form of Levy flights. On the contrary, due to phototaxis, the moths that are comparatively far from the fittest one will tend to fly towards the best one directly in a big step. These two features correspond to the processes of exploitation and exploration of any metaheuristic optimization method. The phototaxis and Levy flights of the moths can be used to build up a general-purpose optimization method. In order to demonstrate the superiority of its performance, the MS method is further compared with five other state-of-the-art metaheuristic optimization algorithms through an array of experiments on fourteen basic benchmarks, eleven IEEE CEC 2005 complicated benchmarks and seven IEEE CEC 2011 real world problems. The results clearly demonstrate that MS significantly outperforms five other methods on most test functions and engineering cases.

关键词： Metaheuristic algorithms moth search algorithm Swarm intelligence Levy flights Benchmark functions Real world problems Performance analysis

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Optimal parameters estimation of PEMFCs model using Converged moth search algorithm

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ENERGY REPORTS 2020年 6卷 1501-1509页

作者： Sun, Shouqiang Su, Yumei Yin, Chengbo Jermsittiparsert, Kittisak Qingdao Huanghai Univ Int Business Sch Qingdao 266427 Shandong Peoples R China Qingdao Huanghai Univ Sch Construct Engn Qingdao 266427 Shandong Peoples R China Qingdao Huanghai Univ Coll Big Data Qingdao 266427 Shandong Peoples R China Chulalongkorn Univ Social Res Inst Bangkok 10330 Thailand

One important part of designing and manufacturing of the fuel cells is their model identification. The present study proposes an optimal method for optimal parameter estimation of the undetermined parameters in Proton Exchange Membrane Fuel Cells (PEMFCs). The method uses a novel modified version of the moth search algorithm, called Converged moth search algorithm (CMSA) to minimize the total of the squared deviations (TSD) between the output voltage and the experimental data. The method is then applied to two different test cases including BCS 500-W PS6 and NedStack PS6. The results show that the suggested CMSA has a TSD and running time equal to 0.012 and 2.96 for BCS and 2.15 and 3.19 for the NedStack that are the minimum values for both case studies toward the other compared algorithms. therefore, the results showed that the suggested method has a good data agreement with the experimental data. (C) 2020 The Authors. Published by Elsevier Ltd.

关键词： PEMFC A total of the squared deviations Optimal parameter estimation moth search algorithm

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A binary moth search algorithm based on self-learning for multidimensional knapsack problems

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FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE 2022年 126卷 48-64页

作者： Feng, Yanhong Wang, Gai-Ge Hebei GEO Univ Sch Informat Engn Shijiazhuang 050031 Hebei Peoples R China Hebei GEO Univ Intelligent Sensor Network Engn Res Ctr Hebei Pro Shijiazhuang 050031 Hebei Peoples R China Hebei GEO Univ Hebei Ctr Ecol & Environm Geol Res Shijiazhuang 050031 Hebei Peoples R China Ocean Univ China Dept Comp Sci & Technol Qingdao 266100 Peoples R China Jilin Agr Univ Coll Informat Technol Changchun 130118 Peoples R China Jilin Univ Minist Educ Key Lab Symbol Computat & Knowledge Engn Changchun 130012 Peoples R China

A key issue for moth search algorithm (MS) is the ability to maintain sufficient diversity in the population so as to be able to track a dynamically changing landscape. However, for the original MS algorithm, the implicit learning mechanism of flight straightly operator focuses mainly on the global best individual, which inevitably causes a deteriorated evolution. To increase the population diversity and enhance the global search ability of MS, a binary moth search algorithm based on self-learning (SLMS) is proposed to solve an NP-hard combinatorial optimization problem with many diverse applications, the 0-1 multidimensional knapsack problem (MKP). In SLMS, a self-learning flight straightly operator is proposed to make each individual learn from any one better than itself, not just the global best individual. The performance of the proposed SLMS is validated by testing 89 different widely used benchmark instances. Experimental results show the effectiveness of the proposed SLMS in solving the MKP problem and the self-learning flight straightly operator can enhance population diversity and global search ability. The effect of transfer functions on the performance of SLMS is also investigated. (C) 2021 Elsevier B.V. All rights reserved.

关键词： Combinatorial optimization Multidimensional knapsack problem moth search algorithm Self-learning Quick repair operator

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Frequent itemset-based feature selection and Rider moth search algorithm for document clustering

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JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES 2022年第4期34卷 1098-1109页

作者： Yarlagadda, Madhulika Rao, K. Gangadhara Srikrishna, A. JNTUK Dept Comp Sci & Engn Kakinada Andhra Pradesh India RVRJC Coll Engn Dept Informat Technol Chowdavaram Andhra Pradesh India Acharya Nagarjuna Univ Dept Comp Sci & Engn Guntur Andhra Pradesh India

Document clustering has recently been paid great attention in retrieval, navigation, and summarization of huge volumes of documents. With a better document clustering approach, computers can organize a document corpus automatically to a meaningful cluster for enabling efficient navigation, and browsing of the corpus. Document navigation and browsing is a valuable complement to the deficiencies of informa-tion retrieval technologies. This paper introduces Modsup-based frequent itemset and Rider Optimization-based moth search algorithm (Rn-MSA) for clustering the documents. At first, the input documents are given to the pre-processing step, and then, the extraction is carried out based on TF-IDF and Wordnet features. Once the extraction is done, the feature selection is carried out based on fre-quent itemset for the establishment of feature knowledge. At last, the document clustering is done using the proposed Rn-MSA, which is designed by combining Rider Optimization algorithm (ROA), and the moth search algorithm (MSA). The performance of the document clustering based on proposed Modsup + Rn-MSA is evaluated in terms of precision, recall, F-Measure, and accuracy. The developed doc-ument clustering method achieves the maximal precision of 95.90%, maximal recall of 96.41%, maximal F-Measure of 96.41%, and the maximal accuracy of 95.12% that indicates its superiority.(c) 2019 The Authors. Production and hosting by Elsevier B.V. on behalf of King Saud University. This is an open access article under the CC BY-NC-ND license (http://***/licenses/by-nc-nd/4.0/).

关键词： Document clustering moth search algorithm Rider Optimization algorithm Frequent itemset Stop word removal

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Wireless Sensor Network Localization Problem by Hybridized moth search algorithm 14

Wireless Sensor Network Localization Problem by Hybridized M...

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14th IEEE International Wireless Communications and Mobile Computing Conference (IWCMC)

作者： Strumberger, Ivana Tuba, Eva Bacanin, Nebojsa Beko, Marko Tuba, Milan Singidunum Univ Fac Informat & Comp Belgrade Serbia Univ Lusofona Humanidades & Tecnol CICANT CIC DIGITAL Lisbon Portugal

ISBN: (纸本)9781538620700

Wireless sensor networks are widely used and consequently represent an important research field. The objective of the node localization problem, that belongs to the group of NP-hard tasks, is to find geographical coordinates of each sensor node with unknown position that are randomly deployed in the monitoring area. Such hard optimization problems are successfully solved by the swarm intelligence algorithms. This paper presents hybridized recent swarm intelligence moth search algorithm adapted for solving localization problem in wireless sensor networks. The application of the moth search algorithm for node localization problem was not found in the literature survey. In the experimental section of this paper we show comparative analysis between the original and hybridized moth search algorithm, as well as with other state-of-the-art algorithms that were tested on the same problem instances of node localization problem. According to experimental results, both, basic moth search and hybridized moth search algorithms are promising approaches for dealing with this kind of problem.

关键词： moth search algorithm hybridization metaheuristic optimization wireless sensor networks node localization problem swarm intelligence

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Optimization of Bridges Reinforcements with Tied-Arch Using moth search algorithm 8th

Optimization of Bridges Reinforcements with Tied-Arch Using ...

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8th International Work-Conference on the Interplay Between Natural and Artificial Computation (IWINAC)

作者： Carrasco, Oscar Crawford, Broderick Soto, Ricardo Lemus-Romani, Jose Astorga, Gino Salas-Fernandez, Agustin Pontificia Univ Catolica Valparaiso Valparaiso Chile Univ Valparaiso Valparaiso Chile

ISBN: (纸本)9783030196516;9783030196509

The deterioration in the bridges that cross the watercourses is a situation that must be resolved in a timely manner to avoid the collapse of its structure. Its repair can mean a high cost, road and environmental alteration. An effective solution, which minimizes this impact, is the installation of a superstructure in the form of an arch that covers the entire length of the bridge and which, by means of a hook anchored to the deck of the bridge, allows the arch to support the weight. This structure must try to maintain the original properties of the bridge, so the calculation of the magnitude of tension of the hangers and the order in which it is applied should not cause damage to the structure. In this document, we propose to optimize the process of calculating the hanger magnitudes and the order in which they must be applied using the moth search algorithm, in order to obtain one or several satisfactory solutions. Finally, we present the results obtained for an arch bridge and three hangers and, thus, evaluate the efficiency and effectiveness in the process of obtaining results in comparison with the Black Hole algorithm.

关键词： Reinforcement of bridges Metaheuristics moth search algorithm Combinatorial optimization

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Modified Patch Antenna Design Using moth search algorithm for RF Energy Harvesting Applications

Modified Patch Antenna Design Using Moth Search Algorithm fo...

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International Workshop on Antenna Technology (iWAT)

作者： Boursianis, Achilles D. Koulouridis, Stavros Rocca, Paolo Goudos, Sotirios K. Aristotle Univ Thessaloniki Dept Phys ELEDIA Res Ctr ELEDIA AUTH Thessaloniki Greece Univ Patras Elect & Comp Engn Dept Patras Greece Univ Trento ELEDIA UniTN DISI Trento Italy

ISBN: (纸本)9781728126401

Radio frequency energy harvesting is a well promising technique to power wireless sensor network devices that require small amounts of energy to operate. It can be utilized as an alternative technique for power-constrained systems, such as wireless sensor networks with limited battery lifetime, by extending their energy supply. In outdoor environments, the dominant ambient radio wave sources are the broadcasting and the mobile communication networks. In this paper, we apply an optimization technique by utilizing moth search algorithm to design a modified microstrip patch antenna of three varying slots. The proposed antenna exhibits tuning operation in the LoRa (Long Range) and the cellular communications frequency bands of GSM-1800 and UMTS. Numerical results exhibit an acceptable performance of the proposed antenna for RF energy harvesting applications.

关键词： patch antenna RF energy harvesting moth search algorithm microstrip antenna optimization

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Enhanced moth search algorithm for the Set-Union Knapsack Problems

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IEEE ACCESS 2019年 7卷 173774-173785页

作者： Feng, Yanhong Yi, Jiao-Hong Wang, Gai-Ge Hebei GEO Univ Sch Informat Engn Shijiazhuang 050031 Hebei Peoples R China Hebei GEO Univ Hebei Ctr Ecol & Environm Geol Res Shijiazhuang 050031 Hebei Peoples R China Qingdao Univ Technol Sch Informat & Control Engn Qingdao 266520 Peoples R China Ocean Univ China Coll Informat Sci & Engn Qingdao 266100 Peoples R China

As an important and novel model with multitudinous practical applications, the set-union knapsack problem (SUKP) is a challenging issue in combinatorial optimization. In this paper, we present an enhanced moth search algorithm (EMS) for solving SUKP, which introduces an enhanced interaction operator (EIO) by integrating differential mutation into the global harmony search and then Levy flight is replaced by EIO. Comparative experimental results, which were conducted on three types of 30 popular SUKP benchmark instances, demonstrate that EMS algorithm is superior to or competitive with the other state-of-the-art metaheuristic algorithm. In particular, EMS reaches the best-known solutions for the great majority of test instances and improves the best-known solutions for six instances. Two critical ingredients of EIO is investigated to confirm their impact on the performance of EMS. The results show that both components have an important role in improving the performance of EMS.

关键词： Differential mutation global harmony search moth search algorithm set-union knapsack problem

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Optimal parameter identification of triple-junction photovoltaic panel based on enhanced moth search algorithm

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ENERGY 2019年 188卷 116025-000页

作者： Fathy, Ahmed Abd Elaziz, Mohamed Sayed, Enas Taha Olabi, A. G. Rezk, Hegazy Jouf Univ Dept Elect Engn Fac Engn Sakakah Saudi Arabia Zagazig Univ Elect Power & Machine Dept Fac Engn Zagazig Egypt Zagazig Univ Dept Math Fac Sci Zagazig Egypt Univ Sharjah Ctr Adv Mat Res POB 27272 Sharjah U Arab Emirates Minia Univ Dept Chem Engn Elminia Egypt Univ Sharjah Dept Sustainable & Renewable Energy Engn POB 27272 Sharjah U Arab Emirates Aston Univ Mech Engn & Design Sch Engn & Appl Sci Aston Triangle Birmingham B4 7ET W Midlands England Prince Sattam Bin Abdulaziz Univ Coll Engn Wadi Addawaser Jeddah Saudi Arabia Minia Univ Dept Elect Engn Fac Engn Al Minya Egypt

The paper proposes an enhanced moth search algorithm (EMSA) employed in identifying the optimal parameters of Triple-Junction (TJS) photovoltaic panel under different operating conditions. Disruptor operator (DO) is placed in the moth search algorithm (MSA) to improve its performance. The DO is used to improve the diversity of the MSA and avoid it from stuck in local point. The presented fitness function in this work is the integral time absolute error (ITAE) between the triple junction PV panel experimental and calculated currents. The panel is simulated in Simulink and tested under different solar radiation conditions. Additionally, the panel performance is investigated under the shadow effect;a comparative study is performed with other metaheuristic optimization approaches and with Hammerstein and wiener identification technique. The proposed EMSA operates with efficiencies around 99.66% and 99.89%for first and second patterns respectively. It is confirmed the superiority and reliability of the proposed EMSA in extracting the optimal parameters of TJS based module operated at different operating conditions. (C) 2019 Elsevier Ltd. All rights reserved.

关键词： Triple-junction solar cell moth search algorithm Disruptor operator

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An adaptive search mechanism with convolutional learning networks for online social media text summarization and classification model

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SCIENTIFIC REPORTS 2025年第1期15卷 1-22页

作者： AL-anazi, Reema G. Alzaidi, Muhammad Swaileh A. Eltahir, Majdy M. Alkhiri, Hassan Alajmani, Samah Hazzaa Darem, Abdulbasit A. Alshahrani, Mohammed Alhebaishi, Nawaf Princess Nourah bint Abdulrahman Univ Coll Humanities & Social Sci Dept Arab Language & Literature POB 84428 Riyadh 11671 Saudi Arabia King Saud Univ Coll Language Sci Dept English Language POB 145111 Riyadh Saudi Arabia King Khalid Univ Appl Coll Mahayil Dept Informat Syst Abha Saudi Arabia Al Baha Univ Fac Comp & Informat Technol Dept Comp Sci Al Baha Saudi Arabia Taif Univ Coll Comp & Informat Technol Dept Informat Technol POB 11099 Taif 21944 Saudi Arabia Northern Border Univ Ctr Sci Res & Entrepreneurship Ar Ar 73213 Saudi Arabia Najran Univ Appl Coll Comp Dept Najran 66462 Saudi Arabia King Abdulaziz Univ Fac Comp & Informat Technol Dept Informat Syst Jeddah Saudi Arabia

The fast development of social media platforms has led to an unprecedented growth of daily short text content. Removing valued patterns and insights from this vast amount of textual data requires advanced methods to provide information while preserving its essential components successfully. A text summarization system takes more than one document as input and tries to give a fluent and concise summary of the most significant information in the input. Recent solutions for condensing and reading text are ineffective and time-consuming, provided plenty of information is available online. Concerning this challenge, automated text summarization methods have developed as a convincing choice, achieving important significance in their growth. It was separated into two kinds according to the abstraction methods utilized: abstractive summarization (AS) and extractive summarization (ES). Furthermore, automatic text summarization has many applications and spheres of impact. This manuscript proposes an Adaptive search Mechanism Based Hierarchical Learning Networks for Social Media Data Summarization and Classification Model (ASMHLN-SMDSCM) technique. The ASMHLN-SMDSCM approach aims to present a novel approach for text summarization on social media using advanced deep learning models. To accomplish that, the proposed ASMHLN-SMDSCM model performs text pre-processing, which contains dissimilar levels employed to handle unprocessed data. The BERT model is used for the feature extraction process. Furthermore, the moth search algorithm (MSA)-based hyperparameter selection process is performed to optimize the feature extraction results of the BERT model. Finally, the classification uses the TabNet and convolutional neural network (TabNet + CNN) model. The efficiency of the ASMHLN-SMDSCM method is validated by comprehensive studies using the FIFA and FARMER datasets. The experimental validation of the ASMHLN-SMDSCM method illustrated a superior accuracy value of 98.87% and 98.55% over r

关键词： Adaptive search mechanism Hierarchical learning Social media Data summarization moth search algorithm

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