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Opposition-based moth swarm algorithm

EXPERT SYSTEMS WITH APPLICATIONS 2021年 184卷 115481-115481页

作者： Oliva, Diego Esquivel-Torres, Sara Hinojosa, Salvador Perez-Cisneros, Marco Osuna-Enciso, Valentin Ortega-Sanchez, Noe Dhiman, Gaurav Heidari, Ali Asghar Univ Guadalajara CUCEI Div Elect & Comp Av Revoluc 1500 Guadalajara Jalisco Mexico Tomsk Polytech Univ Sch Comp Sci & Robot Tomsk Russia Govt Bikram Coll Commerce Dept Comp Sci Patiala 147001 Punjab India Univ Tehran Coll Engn Sch Surveying & Geospatial Engn Tehran Iran Natl Univ Singapore Dept Comp Sci Sch Comp Singapore Singapore

Nowadays, resource-optimizing techniques are required in many engineering areas to obtain the most appropriate solutions for complex problems. For this reason, there is a trend among researchers to improve existing swarm-based algorithms through different evolutionary techniques and to create new population-based methods that can accurately explore the feature space. The recently proposed moth swarm algorithm (MSA) inspired by the orientation of moths towards moonlight is an associative learning mechanism with immediate memory that uses Le ' vy mutation to cross-population diversity and spiral movement. The MSA is a population-based method used for tackling complex optimization problems. It presents an adequate capacity for exploration and exploitation trends;however, due to its nature of operators, this type of method is prone to get stuck in sub-optimal locations, which affects the speed of convergence and the computational effort to reach better solutions. To mitigate these shortcomings, this paper proposes an improved MSA that combines opposition-based learning (OBL) as a mechanism to enhance the exploration drifts of the basic version and increase the speed of convergence to obtain more accurate solutions. The proposed approach is called OBMSA. It has been tested for solving three classic engineering design problems (welded beam, tension/compression spring, and pressure vessel designs) with constraints, 19 benchmark functions comprising 7 unimodal, 6 multimodal, and 6 composite functions. Experimental results and comparisons provide evidence that the performance and accuracy of the proposed method are superior to the original MSA. We hope the community utilizes the proposed MSA-based approach for solving other complex problems.

关键词： moth swarm algorithm Opposition-based learning Optimization techniques Metaheuristics

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Day-ahead combined economic and emission dispatch with spinning reserve consideration using moth swarm algorithm for a data centre load

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HELIYON 2021年第9期7卷 e08054页

作者： Ajayi, Oluwafemi Heymann, Reolyn Univ Johannesburg Ctr Collaborat Digital Networks Dept Elect & Elect Engn Sci ZA-2092 Johannesburg South Africa

Dynamic combined economic and emission dispatch is an important task in the power system that examines the optimal allocation of power generation resources that yield the least possible fuel and emission costs. In this study, the moth swarm algorithm has been proposed for solving the combined economic and emission dispatch problem for a 24-hour period. The model has been implemented on a test system made up of a combination of thermal and solar photovoltaic plants, while also considering spinning reserve allocation. The results obtained are presented in comparison with commonly used state-of-the-art methods like moth Flame Optimization, Whale Optimization algorithm, Ant Lion Optimizer, and Tunicate swarm algorithm. Two test systems have been considered in the model implementation. The first system consists of a combination of six thermal plants and thirteen solar plants whose load demand is the hourly energy demand of an anonymous data centre in South Africa. The second test system is made up of three thermal plants and thirteen solar plants to service its unique hourly load demand. Results showed that the proposed MSA gave preference to solar photovoltaic generation over thermal generation given that environmental impact minimization is a major component of the overall objective function. The proposed MSA also scheduled thermal generators to provide the required spinning reserve capacity since solar energy is intermittent in nature. Overall, analyses show that the proposed MSA outperformed the other methods in finding the best fuel, emission, solar generation, and spinning reserve costs that best serve the energy demand of the data centre and the second test system for the 24-hour period.

关键词： Economic dispatch Emission dispatch moth swarm algorithm Solar photovoltaic

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CNCMSA-ERCP: An Innovative Energy-Efficient Clustering Routing Protocol for Improving the Performance of Industrial IoT

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IEEE INTERNET OF THINGS JOURNAL 2025年第9期12卷 11827-11840页

作者： Liu, Xin Cao, Qike Jin, Bo Zhou, Peng Beijing Univ Technol Beijing 100124 Peoples R China Henan Inst Sci & Technol Xinxiang 453003 Henan Peoples R China Univ Coimbra Inst Syst & Robot Dept Elect & Comp Engn P-3030290 Coimbra Portugal Portuguese Natl Acad Sci P-1600477 Lisbon Portugal Xinjiang Coll Sci & Technol Sch Informat Sci & Engn Korla 841000 Xinjiang Peoples R China Yanshan Univ Sch Informat Sci & Engn Qinhuangdao 066004 Peoples R China

As Industry 4.0 rapidly advances, optimizing energy and performance in industrial wireless sensor networks (IWSNs) for the Industrial Internet of Things (IIoT) is critical. Addressing the NP-hard challenge of IWSN clustering, this article introduces a novel multiobjective model to balance energy consumption and enhance service quality. Based on this model, a new energy-saving cluster routing protocol called CNCMSA-ECRP was designed. The protocol comprehensively considers four key indicators: 1) total residual energy;2) average network delay;3) average network packet loss rate;and 4) average distance from cluster heads (CHs) to the base station (BS), with the goal of achieving overall optimization of network performance. The CNCMSA-ECRP protocol employs our newly proposed chaotic niching clone moth swarm algorithm (CNCMSA). The algorithm introduces chaotic mapping operators in the initialization phase and clonal and niching operators in the evolution phase, enhancing the search capability and diversity of the algorithm, as well as improving clustering efficiency and network stability. Comparative experiments with four existing advanced clustering schemes-LEACH-C, EEHCHR, OptGACHE, and ESCVAD-demonstrate that CNCMSA-ECRP achieves at least a 16.38% increase in network lifespan and significantly reduces average data transmission delay and packet loss rate by at least 1.84% and 17.76%, respectively. This research provides a novel solution to the clustering routing problem in IWSN.

关键词： Clustering algorithms Wireless sensor networks Industrial Internet of Things Quality of service Optimization Energy efficiency Routing Routing protocols Energy consumption Delays Chaotic niching clone cluster routing protocol industrial wireless sensor network (IWSN) moth swarm algorithm Quality of Service (QoS)

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Enhancing Cloud Task Scheduling With a Robust Security Approach and Optimized Hybrid POA

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IEEE ACCESS 2023年 11卷 122426-122445页

作者： Kumer, S. V. Aswin Prabakaran, N. Mohan, E. Natarajan, Balaji Sambasivam, G. Tyagi, Vaibhav Bhushan Koneru Lakshmaiah Educ Fdn Dept Elect & Commun Engn Vaddeswaram 522302 Andhra Pradesh India SIMATS Saveetha Sch Engn Dept ECE Chennai 602105 Tamil Nadu India Sri Venkateshwaraa Coll Engn & Technol Dept Comp Sci & Engn Pondicherry 605102 India Xiamen Univ Malaysia Sch Comp & Data Sci Sepang 43900 Selangor Darul Malaysia ISBAT Univ Fac Engn Kampala Uganda

Dynamic and flexible computing resources are offered by cloud computing (CC), which has gained popularity as a computing technology. Efficient task scheduling (TS) plays a critical role in CC by optimizing the distribution of tasks across available resources to achieve maximum performance. The allocation of computational tasks in a cloud environment is a complicated process that is affected by multiple factors, such as available network bandwidth, make span, and cost considerations. Therefore, it is crucial to optimize available bandwidth for efficient TS in CC. In the present research, a novel pelican-based approach is introduced to optimize TS in the CC environment. The newly developed method also utilizes a security approach called Polymorphic Advanced Encryption Standard (P-AES) to encode cloud information during scheduling. The study evaluates the proposed algorithm's performance in terms of the make span, resource utilization, cost, response time, throughput, latency, execution time, speed, and bandwidth utilization. The simulation is carried out using the Python tool, and it effectively handles a wide range of tasks from 1000 to 5000. The proposed algorithm offers a new perspective on utilizing pelican algorithms to optimize task scheduling in CC. The hybrid optimization enables the proposed algorithm to provide efficient task scheduling by exploiting the strengths of entire algorithms. The proposed approach offers an innovative solution to the challenges of scheduling tasks in cloud environments and provides a more effective and secure way of optimizing cloud services. Overall, this study provides valuable insights into task scheduling optimization in CC and offers an effective approach for enhancing the performance of CC services.

关键词： Task analysis Cloud computing Processor scheduling Dynamic scheduling Optimization Resource management Heuristic algorithms Encryption Particle swarm optimization Advanced encryption standard chameleon swarm algorithm cloud computing hybrid model security moth swarm algorithm task scheduling

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Optimal Luminosity and Contrast Reformation System for Retinal Fundus Image Intensity Enhancement

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WIRELESS PERSONAL COMMUNICATIONS 2024年第1期139卷 301-325页

作者： Kumar, Ravi Bhandari, Ashish Kumar Chouksey, Mausam Natl Inst Technol Patna Dept Elect & Commun Engn Patna Bihar India Muzaffarpur Inst Technol Dept Elect & Commun Engn Muzaffarpur Bihar India Univ Petr & Energy Studies Sch Comp Sci Dehra Dun 248007 Uttaranchal India

Image improvement is essential in various computer vision and image analysis applications. These include healthcare, automotive, security and surveillance, retail and E-commerce, agriculture, manufacturing, entertainment, media, sports activity, and documents and text analysis. Ophthalmologists commonly use fundus images to detect and identify frequent fundus disorders and diseases. Few retinal images are clinically unacceptable due to improper imaging processes and movement of the eyes. The proposed method suggested a new approach for enhancing the contrast and luminosity of fundus pictures. The Luminance Gain Matrix (LGM) is first acquired using the moth swarm algorithm (MSA) of the Value channel (V), which corresponds to the HSV color model. LGM is imposed to improve the RGB channels, respectively. Second, the Contrast-Limited Adaptive Histogram Equalization (CLAHE) enhances the contrast of the Luminosity channel (L) in the L*a*b* color model. The execution of the suggested technique is primarily validated using two datasets: a STARE large dataset of approximately 400 images and a small dataset of 23 high-resolution photos representing the diseases fibrosis and glaucoma, respectively. The proposed approach outshines other state-of-the-art techniques concerning both subjective and objective assessment. As a result, the proposed method may aid ophthalmologists in more effective retinal disease research.

关键词： Contrast enhancement moth swarm algorithm Luminosity Retinal image Ophthalmology

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Intelligent Smart Grid Stability Predictive Model for Cyber-Physical Energy Systems

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Computer Systems Science & Engineering 2023年第2期44卷 1219-1231页

作者： Ashit Kumar Dutta Manal Al Faraj Yasser Albagory Mohammad zeid M Alzamil Abdul Rahaman Wahab Sait Department of Computer Science and Information Systems College of Applied SciencesAlMaarefa UniversityAd DiriyahRiyadh13713Kingdom of Saudi Arabia Department of Computer Engineering College of Computers and Information TechnologyTaif UniversityTaif21944Kingdom of Saudi Arabia Department of Archives and Communication King Faisal UniversityAl AhsaHofuf31982Kingdom of Saudi Arabia

A cyber physical energy system(CPES)involves a combination of pro-cessing,network,and physical *** smart grid plays a vital role in the CPES model where information technology(IT)can be related to the physical *** the same time,the machine learning(ML)modelsﬁnd useful for the smart grids integrated into the CPES for effective decision ***,the smart grids using ML and deep learning(DL)models are anticipated to lessen the requirement of placing many power plants for electricity *** this aspect,this study designs optimal multi-head attention based bidirectional long short term memory(OMHA-MBLSTM)technique for smart grid stability predic-tion in *** proposed OMHA-MBLSTM technique involves three subpro-cesses such as pre-processing,prediction,and hyperparameter *** OMHA-MBLSTM technique employs min-max normalization as a pre-proces-sing ***,the MBLSTM model is applied for the prediction of stability level of the smart grids in *** the same time,the moth swarm algorithm(MHA)is utilized for optimally modifying the hyperparameters involved in the MBLSTM *** ensure the enhanced outcomes of the OMHA-MBLSTM technique,a series of simulations were carried out and the results are inspected under several *** experimental results pointed out the better outcomes of the OMHA-MBLSTM technique over the recent models.

关键词： Stability prediction smart grid cyber physical energy systems deep learning data analytics moth swarm algorithm

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Optimal Placement of Distributed Energy Resources Including Different Load Models Using Different Optimization Techniques

Optimal Placement of Distributed Energy Resources Including ...

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International Conference on Innovative Trends in Computer Engineering (ITCE)

作者： Mohamed, Al-Attar Ali Ali, Shimaa Mohamed Hemeida, A. M. Ibrahim, Abdalla Ahmed Aswan Univ Fac Engn Aswan Egypt Aswan Univ Fac Energy Engn Aswan Egypt

ISBN: (纸本)9781538652619

This article introduces how to improve the real power losses and voltage deviation in distributed network system due to Distributed Energy Resources (DERs) allocation strategy by using moth swarm algorithm (MSA) and Differential Evolution (DE) algorithm. The proposed Optimization techniques are implemented on 33-bus and 69-bus IEEE standard radial distribution test systems with constant, industrial, residential, and commercial loads. The evaluated results have been confirmed the superiority with high performance of the DE technique than MSA technique to find the best solutions of DERs units allocation for different load factors.

关键词： Different load factors models moth swarm algorithm Distributed Energy Resources and power losses reduction

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Maximize the penetration level of photovoltaic systems and shunt capacitors in distribution systems for reducing active power loss and eliminating conventional power source

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SUSTAINABLE ENERGY TECHNOLOGIES AND ASSESSMENTS 2022年第PartC期52卷

作者： Le Chi Kien Thuan Thanh Nguyen Tan Minh Phan Thang Trung Nguyen Ho Chi Minh City Univ Technol & Educ Fac Elect & Elect Engn Ho Chi Minh City 700000 Vietnam Ind Univ Ho Chi Minh City Fac Elect Engn Technol Ho Chi Minh City 700000 Vietnam Ton Duc Thang Univ Fac Elect & Elect Engn Power Syst Optimizat Res Grp Ho Chi Minh City 700000 Vietnam

In this paper, photovoltaic systems and shunt capacitors are optimally placed in three IEEE distribution systems. moth swarm algorithm, Equilibrium optimization and Coot optimization algorithm are used to find the numbers, locations and sizes of the added electric components so that total active power loss is minimum and all constraints are satisfied. As reaching optimal solutions, voltage of nodes and current of conductors satisfy operating ranges while all added photovoltaic systems and all shunt capacitors respectively supply enough active and reactive power demands to loads. Among the three applied algorithms, moth swarm algorithm is more complicated but it is much more effective than two others. In fact, it can reach less active power loss by 76.3% as compared to others and by 86.52%, 83.3% and 94.48% as compared to base systems. Thus, it concludes that the placement of photovoltaic systems and shunt capacitors can replace the conventional power source and moth swarm algorithm is an effective approach in optimizing the placement of both photovoltaic systems and shunt capacitors.

关键词： Active power loss Reactive power loss Photovoltaic systems Shunt capacitors moth swarm algorithm

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Using modern heuristic algorithms for optimal control of a gas lifted field

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JOURNAL OF PETROLEUM SCIENCE AND ENGINEERING 2019年 183卷 106348-000页

作者： Mahdiani, Mohammad Reza Khamehchi, Ehsan Suratgar, Amir Abolfazl Amirkabir Univ Technol Tehran Polytech Dept Petr Engn Hafez Ave Tehran Iran Amirkabir Univ Technol Tehran Polytech Dept Elect Engn Hafez Ave Tehran Iran

In the gas lift, gas injection rate has an optimum value and increasing or decreasing its amount decreases the oil production rate. This optimum point changes in production time and creates an optimum trajectory. There has previously been much research in this area, but none have used a powerful tool in a dynamic model to find a good optimal path for maximizing the production or NPV in a gas-lifted field. In addition, gas allocation in a time step changes the production of different wells and the pattern of fluid flow and also reservoir pressure decline in the reservoir. This can affect the optimum gas allocation of the next time step, but this has not been analyzed in any of the previous works. In this paper, first, a model of reservoir and well of a gas lift well is developed and using some experimental data points the best-fitted correlations are selected. Then, two common and famous heuristic algorithms (genetic algorithm, simulated annealing), and two recently introduced optimization algorithms (moth swarm algorithm and Grasshopper Optimization algorithm) are used to find the optimal path of the injection lift gas rates of the wells. Finally, the results of these four algorithms are compared with each other and some other allocation scenarios. In addition, different aspects of the injection and production rates of the different wells and their cumulative rates and NPV paths are analyzed. The results illustrated that using MSA has an optimum point with higher production. In addition, MSA finds a specific control path with a need for lower lift gas and also a smaller compressor. It also has a really different control path with other optimization algorithm paths. In addition, in this paper, a new long term instability in flow is observed, which was explained by the drainage area of each well. This control path of each well was discussed and it was concluded that GA can find the most stable path.

关键词： Genetic algorithm Grasshopper optimization algorithm moth swarm algorithm Simulated annealing

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Data on optimization of the Karun-4 hydropower reservoir operation using evolutionary algorithms

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DATA IN BRIEF 2020年 29卷 105048页

作者： Akbarifard, Saeid Sharifi, Mohammad Reza Qaderi, Kourosh Shahid Chamran Univ Ahvaz Fac Water Sci Engn Dept Hydrol & Water Resources Water Resources Engn Ahvaz Iran Shahid Chamran Univ Ahvaz Fac Water Sci Engn Dept Hydrol & Water Resources Ahvaz Iran Shahid Bahonar Univ Kerman Dept Water Engn Fac Agr Kerman Iran

This article describes the time series data for optimizing the hydropower operation of the Karun-4 reservoir located in Iran for a period of 106 months (from October 2010 to July 2019). The utilized time-series data included reservoir inflow, reservoir storage, evaporation from the reservoir, precipitation on the reservoir, and release of water through the power plant. In this data article, a model based on moth swarm algorithm (MSA) was developed for the optimization of water resources. The analysis showed that the best solutions achieved by the MSA, Genetic algorithm (GA), and Particle swarm Optimization (PSO) were 0.147, 0.3026, and 0.1584, respectively. The analysis of these datasets revealed that the MSA algorithm was superior to GA and PSO algorithms in the optimal operation of the hydropower reservoir problem. (C) 2020 The Authors. Published by Elsevier Inc.

关键词： Optimization algorithms Karun-4 reservoir Hydropower operation moth swarm algorithm

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