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Energy-Efficient Bi-Objective Optimization Based on the moth-flame algorithm for Cluster Head Selection in a Wireless Sensor Network

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PROCESSES 2023年第2期11卷 534页

作者： Mistarihi, Mahmoud Z. Salameh, Haythem A. Bany Alsaadi, Mohammad Adnan Beyca, Omer F. Heilat, Laila Al-Shobaki, Raya Yarmouk Univ Hijjawi Fac Engn Technol Irbid 21163 Jordan Liwa Coll Technol Fac Engn Dept Mech & Ind Engn POB 41009 Abu Dhabi U Arab Emirates Al Ain Univ Coll Engn POB 64141 Al Ain U Arab Emirates Istanbul Tech Univ Fac Engn Ind Engn Dept TR-34469 Istanbul Turkiye

Designing an efficient wireless sensor network (WSN) system is considered a challenging problem due to the limited energy supply per sensor node. In this paper, the performance of several bi-objective optimization algorithms in providing energy-efficient clustering solutions that can extend the lifetime of sensor nodes were investigated. Specifically, we considered the use of the moth-flame Optimization (MFO) algorithm and the Salp Swarm algorithm (SSA), as well as the Whale Optimization algorithm (WOA), in providing efficient cluster-head selection decisions. Compared to a reference scheme using the Low-Energy Adaptive Clustering Hierarchy (LEACH) protocol, the simulation results showed that integrating the MFO, SSA or WOA algorithms into WSN clustering protocols could significantly extend the WSN lifetime, which improved the nodes' residual energy, the number of alive nodes, the fitness function and the network throughput. The results also revealed that the MFO algorithm outperformed the other algorithms in terms of energy efficiency.

关键词： moth-flame algorithm bi-objective optimization salp swarm algorithm whale optimization algorithm LEACH protocol

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Emergency Surgical Scheduling Model Based on moth-flame Optimization algorithm

Emergency Surgical Scheduling Model Based on Moth-flame Opti...

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International Conference on Computing, Networking and Communications (ICNC)

作者： Huang, Cuiting Ye, Sicong Shuai, Shi Wei, Mengdi Zhou, Yehong Aibin, Anna Aibin, Michal Northeastern Univ Khoury Coll Comp Sci Vancouver BC Canada British Columbia Inst Technol Vancouver BC Canada

ISBN: (纸本)9781665457194

In this paper, we propose an optimization approach based on an improved moth flame Optimization (MFO) algorithm for solving emergency operating room scheduling problems. The purpose of the MFO is to minimize the maximum span of operations, ensuring patients receive their surgeries in a timely manner. This nature-inspired algorithm stimulates the moth's special navigation method at night called transverse orientation. The moth uses the moonlight to sustain a fixed angle to the moon, therefore, guaranteeing a straight line. However, a light source can cause a useless or deadly spiral fly path for moths. The results show that MFO has advantages over Grey Wolf Optimization (GWO) and Genetic algorithm (GA), particularly when comparing the performance of the algorithms under different spiral curves when considering the unrestricted use of surgical beds between different procedures and the optimization of algorithm speed.

关键词： cloud computing moth-flame algorithm scheduling

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A Scheme of Color Image Multithreshold Segmentation Based on Improved moth-flame algorithm

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IEEE ACCESS 2020年 8卷 174142-174159页

作者： Nguyen, Trong-The Wang, Hong-Jiang Dao, Thi-Kien Pan, Jeng-Shyang Ngo, Truong-Giang Yu, Jie Fujian Univ Technol Fujian Prov Key Lab Big Data Min & Applicat Fuzhou 350118 Peoples R China Haiphong Univ Management & Technol Dept Informat Technol Haiphong 180000 Vietnam Thuyloi Univ Fac Comp Sci & Engn Hanoi 116705 Vietnam Fujian Univ Technol Coll Mech & Automot Engn Fuzhou 350118 Peoples R China Shandong Univ Sci & Technol Coll Comp Sci & Engn Qingdao 266510 Peoples R China

A recently developed swarm intelligence algorithm by studying the natural moth's biological behavior is called moth-flame Optimization (MFO). The advantages of MFO conclude a simple structure and a robust selection capability. Still, it is easy to be trapped falling into optimal local, and slow search converges. This study suggests a new process improving MFO by hybridizing Levy flight and logarithmic functions for its formula of flame updating to enhance the optimization performance of the algorithm. In the experimental section, a set of benchmark functions of CEC2013 and the multi threshold image segmentation are used to evaluate the proposed method performance. Compared results of the proposed methods with the different algorithms in the same condition scenarios show that the suggested approach provides better results than the various algorithms in the competitions.

关键词： Image segmentation Fires Optimization Color Particle swarm optimization Linear programming Licenses moth-flame algorithm color image segmentation multi threshold segmentation minimum cross-entropy

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A new technique for influence maximization on social networks using a moth-flame optimization algorithm

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HELIYON 2023年第11期9卷 e22191页

作者： Cui, Qi Liu, Feng Shenyang Agr Univ Coll Econ & Management Shenyang 111000 Liaoning Peoples R China

In our modern digital era, social networks have seamlessly integrated into the fabric of our daily lives. These digital platforms serve as vital channels for communication, exchanging information, and cultivating valuable connections. The propagation of information within these social networks has emerged as a central focus for numerous sectors, including politics, marketing, research, education, and finance. Diverse models have been employed to depict the dynamics of information dissemination across these networks. Nevertheless, the notion of influence holds profound significance for both businesses and individuals. Influence maximization, particularly within the context of social networks, has garnered considerable attention owing to its potential to reach and impact a broad audience. This intricate challenge is commonly referred to as the "influence maximization problem," a problem well-known for its NP-hard complexity. This paper proposes a cutting-edge technique that leverages the moth-flame Optimization algorithm to enhance influence maximization. Influence maximization is an important issue in network analysis, which widely occurs in social networks. Influence can be seen as a cascading effect, where the actions of a few trigger a chain reaction, ultimately reaching a large portion of the network. Identifying these "influencers" is crucial for efficient resource allocation and information dissemination. One of the important issues in finding the maximum influence is choosing the best vertex among all the vertices in the graph. This research presents a new method to find the maximum influence in social networks based on the moth-flame algorithm (MFA). The proposed method aims to find the maximum influence in the social network graph that has a good fitness degree. The algorithm can identify potential influencers. Our simulations across multiple networks have unequivocally showcased the superiority of this algorithm as the preeminent and scalable solution to th

关键词： Maximum influence Graph Social network moth-flame algorithm

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A novel method for high-level synthesis of datapaths in digital filters using a moth-flame optimization algorithm

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EVOLUTIONARY INTELLIGENCE 2020年第3期13卷 399-414页

作者： Esmaeili, Mohammad Reza Zahiri, Seyed Hamid Razavi, Seyed Mohammad Univ Birjand Dept Elect Engn Birjand Iran

High-level synthesis (HLS) is one of the most important processes in digital VLSI circuit design. Owing to complexity and enormity of the design space in HLS problems, employing meta-heuristic methods and swarm intelligence has been considered as a highly favorable option when solving such problems. This research work proposes a moth-flame optimization (MFO) algorithm-based method for HLS of datapaths in digital filters, where scheduling, allocating, and binding steps were performed simultaneously. It was observed that the efficiency of the proposed method enjoyed an improved efficiency thanks to the mentioned simultaneous steps while being combined with the MFO algorithm. By comparing the performance of the proposed method with Genetic algorithm based method and particle swarm optimization based method for HLS of digital filters benchmarks, it can be inferred that the proposed method outperforms the other two methods in HLS of digital filters. This is evidently approved by a maximum improvement observed in the rates of the delay, the occupied area of the chip, and the power consumption for 2.99%, 6.58%, and 6.48%, respectively. In addition to the mentioned improvement, another striking characteristic of the proposed method is its fast runtime in reaching a response. This could significantly lower the costs while increasing the design speed of circuits having large dimensions. As well, an averagely 20% rise was also discerned in the algorithm runtime compared to the other two methods.

关键词： Digital VLSI circuits Datapath Digital filters High-level synthesis moth-flame algorithm Optimization

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Optimal Installation of Multiple DG using Chaotic moth-flame algorithm and Real Power Loss Sensitivity Factor in Distribution System

Optimal Installation of Multiple DG using Chaotic Moth-flame...

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International Conference on Smart Energy Systems and Technologies (SEST)

作者： Abdel-mawgoud, Hussein Kamel, Salah Tostado, Marcos Yu, Juan Jurado, Francisco Aswan Univ Fac Engn Dept Elect Engn Aswan 81542 Egypt Chongqing Univ Coll Elect Engn State Key Lab Power Transmiss Equipment & Syst S Chongqing 400030 Peoples R China Univ Jaen Dept Elect Engn Jaen 23700 Spain

ISBN: (纸本)9781538653265

During recent years, power system planners are becoming interested to integration of decentralized generation in radial distribution system (RDS) to withstand the electric grid issues. Integration of decentralized generation (DG) at non-optimal locations and sizes make the power system issues to be achieved such as increased system power loss and reduced bus system voltage. In this paper, the optimal locations and sizes of renewable DG in RDS are determined using chaotic moth-flame optimization (CMFO) algorithm and real power loss sensitivity factor (PLSF) to achieve the maximum reduction in active system power loss. Integration of renewable distributed generation such as solar (PV) and wind (WTG) in RDS have received great attention in many countries over the world due to its effect in minimizing the distribution feeders' losses and the greenhouse gas emission. The proposed method is applied to one, two and three decentralized generations installation in IEEE thirty three bus RDS. The obtained results proved that, the active system power loss reduction of single, two and three integration of DG are nearly 47.4 %, 58.7 % and 65.5 % for solar (PV) and 67.8 %, 86.5 % and 94.4 % for wind (WTG). From comparative study, the simulation results illustrate the high performance of the proposed technique.

关键词： optimal installation decentralized generation moth-flame algorithm real power loss

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moth-flame optimization algorithm to determine optimal machining parameters in manufacturing processes

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MATERIALS TESTING 2017年第5期59卷 425-429页

作者： Yildiz, Betul Sultan Yildiz, Ali Riza Bursa Tech Univ Dept Mech Engn Bursa Turkey

In this research, a newly developed moth-flame optimization algorithm (MFO) is presented for solving optimization problems in manufacturing industry. A well-known milling optimization problem is solved to emphasize the effectiveness of the MFO in the optimization of manufacturing problems. In the optimization problem solved in this paper, the main aim is to maximize the profit rate for multi-tool milling operations considering difficult constraints. The results demonstrate that the MFO is an effective optimization method for the solution of manufacturing optimization problems.

关键词： moth-flame algorithm optimization milling operation manufacturing

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Improving multilayer perceptron neural network using two enhanced moth-flame optimizers to forecast iron ore prices

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JOURNAL OF INTELLIGENT SYSTEMS 2024年第1期33卷

作者： Doush, Iyad Abu Ahmed, Basem Awadallah, Mohammed A. Al-Betar, Mohammed Azmi Alawad, Noor Aldeen Ajman Univ Artificial Intelligence Res Ctr AIRC Ajman 61001 U Arab Emirates Al Balqa Appl Univ Al Huson Univ Coll Dept Informat Technol POB 50 Irbid 21110 Jordan Amer Univ Kuwait Coll Engn & Appl Sci Dept Comp Salmiya 20002 Kuwait Yarmouk Univ Comp Sci Dept Irbid 21163 Jordan Al Aqsa Univ Dept Comp Sci POB 4051 Gaza Palestine

The quality of the output produced by the multi-layer perceptron neural network depends on the careful selection of its weights and biases. The gradient descent technique is commonly used for choosing MLP's optimal configuration, but it can suffer from being stuck in local optima and slow convergence toward promising regions in the search space. In this article, we propose two new optimization algorithms based on the moth-flame optimization algorithm (MFO), which mimics moths' special navigation methods at night. We use these algorithms to enhance the performance of the training process of the MLP neural network. To demonstrate the effectiveness of our approach, we apply it to the problem of predicting iron ore prices, which plays an important role in the continuous development of the steel industry. We use a large number of features to predict the iron ore price, and we select a promising set of features using two feature reduction methods: Pearson's correlation and a newly proposed categorized correlation. Surprisingly, new features not mentioned in the literature are discovered, and some are discarded. The time series dataset used has been extracted from several sources and pre-processed to fit the proposed model. We compare our two proposed MFO algorithms, the roulette wheel moth-flame optimization algorithm and the global best moth-flame optimization algorithm, against four swarm intelligence algorithms and five classical machine learning techniques when predicting the iron ore price. The results acquired indicate the superior performance of the suggested algorithms concerning prediction accuracy, root-mean-square error, mean-square error, average absolute relative deviation, and mean absolute error. Overall, our work presents a promising approach for improving the performance of MLP neural networks, and it demonstrates its effectiveness in the challenging problem of predicting iron ore prices.

关键词： multilayer perceptron neural network iron ore price prediction predicting training neural network swarm intelligence optimizers moth-flame algorithm

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CNN segmentation of skin melanoma in pre-processed dermoscopy images

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Procedia Computer Science 2024年 235卷 2775-2782页

作者： Seifedine Kadry Elena Verdú Robertas Damasevicius Laith Abualigah Vijendra Singh Venkatesan Rajinikanth Department of Applied Data Science Noroff University College Kristiansand Norway Artificial Intelligence Research Center (AIRC) Ajman University Ajman 346 United Arab Emirates Department of Electrical and Computer Engineering Lebanese American University Byblos Lebanon Higher School of Engineering and Technology Universidad Internacional de La Rioja Logroño 26006 Spain Department of Applied Informatics Vytautas Magnus University 53361 Kaunas Lithuania Computer Science Department Al al-Bayt University Mafraq 25113 Jordan Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman 19328 Jordan MEU Research Unit Middle East University Amman 11831 Jordan School of Computer Sciences Universiti Sains Malaysia Pulau Pinang 11800 Malaysia School of Engineering and Technology Sunway University Malaysia Petaling Jaya 27500 Malaysia School of Computer Science University of Petroleum and Energy Studies Dehradun 248007 India Department of Computer Science and Engineering Division of Research and Innovation Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai 602105 India

Using medical data to improve diagnosis accuracy has recently become common practice in hospitals. A modern computing environment has enabled real-time diagnosis of medical data using Convolutional Neural Networks (CNNs). To extract and evaluate skin melanoma recorded with digital dermatoscopy images (DDI), we developed a CNN segmentation framework. In this proposal, four phases are proposed: (i) DDI collection and resizing, (ii) DDI enhancement using pre-processing techniques, (iii) CNN segmentation for lesion extraction, (v) Comparing the extracted sections to the ground truth images, and (v) Verifying whether the framework is valid. Using DDI pre-processed with (i) Traditional procedures, (ii) Otsu’s thresholding, (iii) Kapur’s thresholding, and (iv) Fuzzy-Tsallis thresholding, this proposal examines the different CNN segmentation schemes presented in the literature. For mining skin lesions, the moth-flame algorithm (MFA) combined with tri-level thresholding achieves an optimal threshold for the DDI. With Fuzzy-Tsallis thresholding images, the VGG-UNet performs better than the alternatives. This framework helps to achieve better values of Jaccard (88.47±2.13%), Dice (93.08±1.17%), and Accuracy (98.64±0.71%) on the chosen DDI database.

关键词： Watershed-algorithm skin melanoma segmentation Fuzzy-Tsallis entropy moth-flame algorithm evaluation

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Optimization of a Hybrid Energy System Architecture in Island Mode by a Two-Layer Approach 10

Optimization of a Hybrid Energy System Architecture in Islan...

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10th Smart Grid Conference (SGC)

作者： Ardabili, Navid Afsari SeyedShenava, SeyedJalal Shayeghi, Hossein Univ Mohaghegh Ardabili Dept Elect & Comp Engn Ardebil Iran

ISBN: (纸本)9781665419574

Hybrid energy systems are an important part of sustainable development. Due to increased interest in optimal using of these systems, having various methods to solve the optimization problem seems to be necessary. This paper proposes a two-layer algorithm to use in a multi-objective optimization of the hybrid energy system planning. In the first layer, the optimal sizes of the hybrid power supply components are calculated by using moth-flame optimization algorithm (MFO). In the second layer, the optimal power flow is calculated by using the mathematical quadratic programming algorithm (QP) to optimize the hybrid energy system operation. Then, it is possible to prioritize the goals and add the extra constraints, by weighting coefficients. The optimization results have been compared to show the effectiveness of the proposed method in various cases.

关键词： Hybrid energy system Optimal size Total annual cost moth-flame algorithm Quadratic programming algorithm

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