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Modified search and rescue optimization algorithm for identifying the optimal parameters of high efficiency triple-junction solar cell/module

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INTERNATIONAL JOURNAL OF ENERGY REsearch 2022年第10期46卷 13961-13985页

作者： Houssein, Essam H. Nassef, Ahmed M. Fathy, Ahmed Mahdy, Mohamed A. Rezk, Hegazy Minia Univ Fac Comp & Informat Al Minya Egypt Prince Sattam Bin Abdulaziz Univ Coll Engn Wadi Addawaser Al Kharj Saudi Arabia Tanta Univ Comp & Automat Control Engn Dept Fac Engn Tanta Egypt Jouf Univ Dept Elect Engn Fac Engn Al Jouf Saudi Arabia Zagazig Univ Elect Power & Machine Dept Fac Engn Zagazig Egypt Beni Suef Univ Fac Comp & Artificial Intelligence Bani Suwayf Egypt Minia Univ Dept Elect Engn Fac Engn Al Minya Egypt

The search and rescue optimization algorithm (SAR) is a recent metaheuristic inspired by the explorations behavior of humans during search and rescue operations. Similar to many of the metaheuristic algorithms (MAs), SAR may suffer from drawbacks, such as being trapped in minimum local regions and improper balance between exploitation and exploration phases. To overcome these weaknesses, this study proposes an alternative algorithm of search and rescue optimization algorithm (SAR) called (mSAR) to improve its diversity and provide a soft balance between the exploration and exploitation stages of the original SAR algorithm. The mSAR introduces an adaptive strategy to boost the algorithm performance. To assess its reliability, the proposed approach is validated through IEEE CEC'2020 test suite against six state-of-the-art algorithms namely the Adaptive guided differential evolution algorithm (AGDE), Evolution strategy with covariance matrix adaptation (CMA-ES), the Whale optimization algorithm (WOA), Harris hawks optimization (HHO), Archimedes optimization algorithm (AOA), Cuckoo search algorithm (CS) besides the original SAR algorithm. A robust strategy based on a mSAR to create an equivalent circuit for a high-efficiency triple-junction solar cell/module (TJS/M) has been proposed. The suggested strategy is used to determine the best parameters of the TJS/M model based on the measurement datasets. During the optimization process, the aim is to minimize the integral time absolute error (ITAE) between the measured and estimated currents. The suggested mSAR is compared with other optimizers considering statistical tests of Wilcoxon sign rank, Friedman, and ANOVA. For the case of TJSC based PV module, the best (minimum) objective function is achieved at a value of 0.00178 by the proposed mSAR, whereas the worst value at 0.02476 is obtained by the CS. Furthermore, for the single TJSC, the minimum objective function is achieved at value of 0.04277 by the proposed mSAR wher

关键词： meta-heuristic algorithms parameter estimation parameter estimation search and rescue optimization algorithm triple-junction solar cell

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A novel UAV path planning approach: Heuristic crossing search and rescue optimization algorithm

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EXPERT SYSTEMS WITH APPLICATIONS 2023年 215卷

作者： Zhang, Chaoqun Zhou, Wenjuan Qin, Weidong Tang, Weidong Guangxi Minzu Univ Coll Artificial Intelligence Nanning 530006 Peoples R China Guangxi Key Lab Hybrid Computat & IC Design Anal Nanning 530006 Peoples R China

Unmanned aerial vehicle (UAV) path planning plays an important role in the flight process of an UAV, which needs an effective algorithm to deal with UAV path planning problem. The search and rescue optimization algorithm (SAR) is easy to implement and has the characteristics of flexible, but it has slow convergence speed and has not been applied to UAV path planning. To address these problems, a heuristic crossing search and rescue optimization algorithm (HC-SAR) is proposed. HC-SAR combines a heuristic crossover strategy with the basic SAR to improve the convergence speed and maintain the population diversity in the optimization process. Furthermore, a real-time path adjustment strategy is proposed to straighten the UAV flight path. In addition, cubic B-spline interpolation is used to smooth the generated path. Comprehensive experiments including two-dimensional and three-dimensional environments for different threat zone are conducted to validate the performance of HC-SAR. The results show that HC-SAR has a high convergence speed and can successfully obtain a safe and efficient path, and it significantly outperforms SAR, differential evolution (DE), ant lion optimizer (ALO), squirrel search algorithm (SSA) and salp swarm algorithm (SSA) in all the cases. These results suggest that the proposed algorithm can effectively serve as an alternative for solving UAV path planning problem.

关键词： Path planning Heuristic crossing search and rescue optimization algorithm search and rescue optimization algorithm Path adjustment Unmanned aerial vehicle

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search and rescue optimization algorithm: A new optimization method for solving constrained engineering optimization problems

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EXPERT SYSTEMS WITH APPLICATIONS 2020年 161卷 113698-113698页

作者： Shabani, Amir Asgarian, Behrouz Salido, Miguel Gharebaghi, Saeed Asil KN Toosi Univ Technol Fac Civil Engn Tehran Iran Univ Politecn Valencia Inst Automat & Informat Ind Valencia Spain

A new optimization method namely the search and rescue optimization algorithm (SAR) is presented here to solve constrained engineering optimization problems. This metaheuristic algorithm imitates the explorations behavior of humans during search and rescue operations. The e-constrained method is utilized as a constraint-handling technique. Besides, a restart strategy is proposed to avoid local infeasible minima in some complex constrained optimization problems. SAR is applied to solve 18 benchmark constraint functions presented in CEC 2010, 13 benchmark constraint functions, and 7 constrained engineering design problems reported in the specialized literature. The performance of SAR is compared with some state-of-the-art optimization algorithms. According to the statistical comparison results, the performance of SAR is better or highly competitive against the compared algorithms on most of the studied problems. (c) 2020 Elsevier Ltd. All rights reserved.

关键词： Constrained optimization Engineering optimization problems search and rescue optimization algorithm Metaheuristic algorithms

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AModified search and rescue optimization Based Node Localization Technique inWSN

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Computers, Materials & Continua 2022年第1期70卷 1229-1245页

作者： Suma Sira Jacob K.Muthumayil M.Kavitha Lijo Jacob Varghese M.Ilayaraja Irina V.Pustokhina Denis A.Pustokhin Department of Computer Science and Engineering Christian College of Engineering and TechnologyOddanchatram624619India Department of Information Technology PSNA College of Engineering and TechnologyDindigul624622India Department of Electronics and Communication Engineering K.Ramakrishnan College of TechnologyTiruchirappalli621112India Department of Electrical and Electronics Engineering Christian College of Engineering and TechnologyOddanchatram624619India School of Computing Kalasalingam Academy of Research and EducationKrishnankoil626128India Department of Entrepreneurship and Logistics Plekhanov Russian University of EconomicsMoscow117997Russia Department of Logistics State University of ManagementMoscow109542Russia

Wireless sensor network(WSN)is an emerging technology which find useful in several application areas such as healthcare,environmentalmonitoring,border surveillance,*** issues that exist in the designing of WSN are node localization,coverage,energy efficiency,security,and so *** spite of the issues,node localization is considered an important issue,which intends to calculate the coordinate points of unknown nodes with the assistance of *** efficiency of the WSN can be considerably influenced by the node localization ***,this paper presents a modified search and rescue optimization based node localization technique(MSRONLT)*** major aim of theMSRO-NLT technique is to determine the positioning of the unknown nodes in *** the traditional search and rescue optimization(SRO)algorithm suffers from the local optima problemwith an increase in number of iterations,MSRO algorithm is developed by the incorporation of chaotic maps to improvise the diversity of the *** application of the concept of chaotic map to the characteristics of the traditional SRO algorithm helps to achieve better exploration ability of the MSRO *** order to validate the effective node localization performance of the MSRO-NLT algorithm,a set of simulations were performed to highlight the supremacy of the presented model.A detailed comparative results analysis showcased the betterment of the MSRO-NLT technique over the other compared methods in terms of different measures.

关键词： Node localization WSN chaotic map search and rescue optimization algorithm localization error

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Fuzzy-based adaptive learning network using search and rescue optimization for e-waste management model: case study

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ENVIRONMENTAL SCIENCE AND POLLUTION REsearch 2022年第14期29卷 19975-19990页

作者： Batoo, Khalid Mujasam Pandiaraj, Saravanan Muthuramamoorthy, Muthumareeswaran Raslan, Emad Krishnamoorthy, Sujatha King Saud Univ Coll Sci King Abdullah Inst Nanotechnol Riyadh Saudi Arabia King Saud Univ CFY Deanship Riyadh Saudi Arabia King Saud Univ Coll Sci Dept Phys Riyadh Saudi Arabia Wenzhou Kean Univ Dept Comp Sci Wenzhou 325060 Zhejiang Peoples R China

In recent days, the expansion of e-waste disposal should be increased due to environmental hazards, contamination of groundwater, an unconcerned consequence on marine life, human health, and decrease in the fertility of the soil. The majority of the developing countries are facing massive issues in implementing sustainable e-waste management schemes. The unofficial e-waste management schemes in the region of Chandigarh, India, have become a serious dispute for the government and several stakeholders due to human health and environmental effects. To overcome such shortcomings, this paper proposes an efficient e-waste management system using fuzzy c-means based adaptive optimal neural network. Here fuzzy c-means clustering approach is employed to classify the household e-wastes and adaptive optimal neural network is employed to analyze the relative weights as well as the grading of the obstructions. Here, the financial and economic limitations are regarded as the most important obstructions of e-waste formalization. The sensitivity analysis is carried out to verify the structure robustness and address the bias effect. This study assists the lawmakers to create organized strategies for an efficient e-waste management system. The sustainable set of e-waste management system advances the e-waste management in India quality thereby raising the recycling rate to 40%.

关键词： Electronic waste Barriers E-waste management Fuzzy C-means Neural network search and rescue optimization algorithm

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Day-ahead optimization for smart energy management of multi-microgrid using a stochastic-robust model

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ENERGY 2024年 313卷

作者： Ge, Haotian Zhu, Yu Zhong, Jiuming Wu, Liang Hainan Normal Univ Coll Phys & Elect Engn Haikou Hainan Peoples R China

The growing popularity of hydrogen fuel cell vehicles (HFCVs) and electric vehicles (EVs) has led to the widespread adoption of multi-energy microgrids (MEMGs), which seamlessly integrate hydrogen refueling station systems (HRSS) and electric vehicle parking lots (EVPLs). Power-to-hydrogen (P2H2) technology has been instrumental in enabling this transition. To further enhance the efficiency and reliability of MEMG systems, a network structure known as a multi-microgrid (MMG) has emerged. This research introduces a robust decentralized framework for energy management, with a focus on optimizing day-ahead planning for interconnected microgrids (MGs). The MMG configuration includes hydrogen provider companies (HPCs) and electricity markets, integrating cutting-edge technologies such as power-to-heat (P2H) units, P2H2 units, combined heat and power (CHP) units, and various energy storage systems (ESSs). Maintaining data privacy is a key concern for interconnected MGs operating within an MMG. To address this, the study proposes the use of a search and rescue optimization (SARO) algorithm, which strengthens local and global search capabilities while safeguarding data privacy. Furthermore, the MMG integrates a demand response program (DRP) that efficiently manages electricity consumption through price signals, leading to greater cost-effectiveness and energy efficiency. Simulation results confirm the effectiveness of the proposed decentralized model in meeting diverse energy requirements, even in challenging scenarios with fluctuating electricity market prices.

关键词： Hydrogen fuel cell vehicles Electric vehicles Multi-energy microgrids Power-to-hydrogen technology Demand response program search and rescue optimization algorithm

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HPWO-LS-based deep learning approach with S-ROA-optimized optic cup segmentation for fundus image classification

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NEURAL COMPUTING & APPLICATIONS 2021年第15期33卷 9677-9690页

作者： Ramya, J. Rajakumar, M. P. Maheswari, B. Uma St Josephs Coll Engn Comp Sci & Engn Chennai Tamil Nadu India

Recently, automated retinal image processing has been considered a competitive field of research due to the low-accuracy results, complexity, and unacceptable outcomes associated with it. In this article, we proposed a novel approach for the classification of fundus images from different kinds of fundus disorders. The original images are preprocessed in terms of noise and contrast enhancement by using the contrast limited adaptive histogram equalization method. The optic cup segmentation from the fundus images is effectively handled via the search and rescue optimization algorithm. After that, the color, texture, and shape-based gray-level co-occurrence matrix features are extracted. The hybrid particle swarm optimization with local search strategy improves the DNN parameter and the newly developed method is named as optimal DNN. The optimal DNN is used to classify whether the image is diabetic retinopathy, glaucoma, or age-related macular degeneration. Experimentally, different kinds of datasets such as STARE, Drishti, and RIM-One datasets with performance measure are validated. Finally, the proposed approaches demonstrate higher classification performances in terms of accuracy, specificity, sensitivity, precision, recall, and f-measure.

关键词： Fundus image Optic cup search and rescue optimization algorithm Hybrid particle swarm optimization with local search Deep neural network

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