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APPLIED SOFT COMPUTING 2015年 36卷 315-333页

作者： Shareef, Hussain Ibrahim, Ahmad Asrul Mutlag, Ammar Hussein United Arab Emirates Univ Dept Elect Engn Al Ain U Arab Emirates

This paper introduces a novel metaheuristic optimization method called the lightning search algorithm (LSA) to solve constraint optimization problems. It is based on the natural phenomenon of lightning and the mechanism of step leader propagation using the concept of fast particles known as projectiles. Three projectile types are developed to represent the transition projectiles that create the first step leader population, the space projectiles that attempt to become the leader, and the lead projectile that represent the projectile fired from best positioned step leader. In contrast to that of the counterparts of the LSA, the major exploration feature of the proposed algorithm is modeled using the exponential random behavior of space projectile and the concurrent formation of two leader tips at fork points using opposition theory. To evaluate the reliability and efficiency of the proposed algorithm, the LSA is tested using a well-utilized set of 24 benchmark functions with various characteristics necessary to evaluate a new algorithm. An extensive comparative study with four other well-known methods is conducted to validate and compare the performance of the LSA. The result demonstrates that the LSA generally provides better results compared with the other tested methods with a high convergence rate. (C) 2015 Elsevier B.V. All rights reserved.

关键词： Benchmark functions Constrained optimization lightning search algorithm Nature-inspired algorithms

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Application of lightning search algorithm in localization of Wireless Sensor Networks 4

Application of lightning search algorithm in localization of...

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4th IEEE International Symposium on Wireless Systems within the International Conferences on Intelligent Data Acquisition and Advanced Computing Systems (IDAACS-SWS)

作者： Liu, Wei Huang, Yongkun Zong, Xinlu Shi, Heng Ye, Zhiwei Wei, Siwei Hubei Univ Technol Wuhan Sch Comp Sci Wuhan Hubei Peoples R China CCCC Second Highway Consultants Co Ltd Wuhan 430052 Hubei Peoples R China

ISBN: (纸本)9781538675878

In the problem of localization in WSN (wireless sensor networks), how to improve the accuracy has been a hot topic of research. Using RSSI ranging to measure distance. Due to the influence of environmental factors and obstacles, there will be a certain error between the measured distance and the actual distance, which will result in an error when the node is positioned. In order to solve this problem, Application of LSA(lightning search algorithm) to WSN localization, and trilateration method is used to localization, to improve the accuracy of localization. In simulation experiments, comparing LSA with GA(genetic algorithm) and general location, the results show that the LSA has achieved good results in the convergence and location accuracy of the algorithm.

关键词： wireless sensor network lightning search algorithm Trilateration

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Minimization of Harmonic Distortion Impact Due to Large-Scale Fast Charging Station Using Modified lightning search algorithm and Pareto-Fuzzy Synergistic Approach

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IEEJ TRANSACTIONS ON ELECTRICAL AND ELECTRONIC ENGINEERING 2018年第6期13卷 815-822页

作者： Nasir, S. N. Syed Jamian, J. J. Mustafa, M. W. Univ Teknol Malaysia Fac Elect Engn Utm Johor Bahru 81310 Johor Malaysia

The increased number of nonlinear loads in the distribution system has caused a significant impact on its power quality in the form of higher harmonic distortion. The fast charging station (FCS) is an example of a powerful nonlinear load that is currently being widely promoted with the aim of supporting the continuous usage of electric vehicles (EV). The most common and cheapest method to minimize harmonic impact is through the use of a passive filter. This paper presents an optimal sizing and placement of a passive filter design based on the numbers of FCS installed in the distribution system. The MATLAB/m-file platform is used to model the power system harmonic flow, passive filter, and FCS. The control strategy used to minimize the total harmonic distortion (THD) due to the large number of FCS is presented based on the selection of optimal location and the size of passive filters that will provide minimum THD and apparent power losses to the overall distribution system. A Modified lightning search algorithm (MLSA) technique is applied with the aim of identifying the optimal solution. The Pareto-Fuzzy technique is used to obtain a better solution among nondominated solutions. From the result, the optimal placements and sizes of passive filter will be able to reduce the maximum THD and also the total losses in the distribution system, which is equipped with 17 FCS units. This study is useful as a guide for grid owners to control the impact of large-scale FCS deployment in the distribution system. (C) 2018 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

关键词： THD improvement fast charging station passive filter lightning search algorithm harmonic analysis reduce losses

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Parameter Extraction of Solar Cell Models Using the lightning search algorithm in Different Weather Conditions

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JOURNAL OF SOLAR ENERGY ENGINEERING-TRANSACTIONS OF THE ASME 2016年第4期138卷 041007页

作者： Sirjani, Reza Shareef, Hussain Cyprus Int Univ Fac Engn Mersin 10 TR-99258 Nicosia Northern Cyprus Turkey United Arab Emirates Univ Dept Elect Engn POB 15551 Al Ain U Arab Emirates

Recently, accurate modeling of the differences between the current and voltage (I-V) characteristics of solar cells has been the main focus of many research studies. Mostly the results were obtained only for single diode or double diode solar cells, not for both or even for photovoltaic (PV) modules. Moreover, the effect of different shading conditions and different temperatures should be considered;otherwise, the obtained results would be reliable for specific weather conditions and unreliable for all real conditions. In this study, a novel nature-inspired optimization method known as the lightning search algorithm (LSA) was developed to extract the parameters of single diode and double diode solar cells as well as for a PV module. LSA is formulated based on lightning, which originates from thunderstorms. Experimental data from multicrystalline KC200GT solar panels were used to test the single diode and double diode solar panel models, and experimental data from the monocrystalline SQ150-PC solar panels were used to test the PV module model. The experimental data are first collected at the same temperature at five different irradiance levels. In the second stage, variations in temperature are considered at the same irradiance level. The extraction results in the LSA I-V curves accurately fit the entire range of the experimental data, while many fluctuations were seen in the particle swarm optimization (PSO) and bee colony optimization (BCO) I-V curves. The convergence characteristics of LSA were also evaluated in terms of accuracy and speed. For all cases, when LSA was used, the accuracies matched well with the entire range of experimental data. In addition, the value of the objective function using LSA was lower, and that method converged much faster than PSO and BCO.

关键词： solar cells photovoltaic parameter extraction optimization lightning search algorithm

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A New Wind Power Model Using the lightning search algorithm 13

A New Wind Power Model Using the Lightning Search Algorithm

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13th HONET-ICT International Symposium on Smart MicroGrids for Sustainable Energy Sources enabled by Photonics and IoT Sensors

作者： Sirjani, Reza Okonkwo, Eric Chekwube Cyprus Int Univ Dept Elect & Elect Engn Northern Cyprus 10 Mersin Turkey Cyprus Int Univ Dept Energy Syst Engn Northern Cyprus 10 Mersin Turkey

ISBN: (纸本)9781509037841

Wind power as a source of renewable energy with low pollution has grown substantially in recent years. Being able to accurately predict the generated power of wind plants is very important for proper operation of the power system. This study presents a novel optimization algorithm for determining the optimal value of the weighting factor in the wind power model, the algorithm known as the lightning search algorithm (LSA) was developed from the natural occurrence of lightning formed by thunderstorms. The model was tested using data collected from the hourly wind power output of the Zabol wind plant in Iran. The obtained wind power model from the algorithm presented an accurate fit for the entire range of the actual data and gave a lower error in comparison with the results obtained using bee colony optimization (BCO) or particle swarm optimization (PSO).

关键词： Wind Energy Power Model Optimization lightning search algorithm

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Optimizing the Learning Process of Feedforward Neural Networks Using lightning search algorithm

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INTERNATIONAL JOURNAL ON ARTIFICIAL INTELLIGENCE TOOLS 2016年第6期25卷

作者： Faris, Hossam Aljarah, Ibrahim Al-Madi, Nailah Mirjalili, Seyedali Univ Jordan King Abdullah Sch Informat Technol 2 Business Informat Technol Dept Amman Jordan Princess Sumaya Univ Technol King Hussein Fac Comp Sci Amman Jordan Griffith Univ Sch Informat & Commun Technol Brisbane Qld 4111 Australia

Evolutionary Neural Networks are proven to be beneficial in solving challenging datasets mainly due to the high local optima avoidance. Stochastic operators in such techniques reduce the probability of stagnation in local solutions and assist them to supersede conventional training algorithms such as Back Propagation (BP) and Levenberg-Marquardt (LM). According to the No-Free-Lunch (NFL), however, there is no optimization technique for solving all optimization problems. This means that a Neural Network trained by a new algorithm has the potential to solve a new set of problems or outperform the current techniques in solving existing problems. This motivates our attempts to investigate the efficiency of the recently proposed Evolutionary algorithm called lightning search algorithm (LSA) in training Neural Network for the first time in the literature. The LSA-based trainer is benchmarked on 16 popular medical diagnosis problems and compared to BP, LM, and 6 other evolutionary trainers. The quantitative and qualitative results show that the LSA algorithm is able to show not only better local solutions avoidance but also faster convergence speed compared to the other algorithms employed. In addition, the statistical test conducted proves that the LSA-based trainer is significantly superior in comparison with the current algorithms on the majority of datasets.

关键词： Optimization metaheuristic lightning search algorithm LSA optimization learning multilayer perceptron neural network

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Developed algorithm Based on lightning search Optimizer and Analytical Technique for Allocation of Distribution Generators 21

Developed Algorithm Based on Lightning Search Optimizer and ...

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21st International Middle East Power Systems Conference (MEPCON)

作者： Selim, Ali Kamel, Salah Jurado, Francisco Marrouchi, Sahbi Univ Jaen Dept Elect Engn Jaen Spain Aswan Univ Dept Elect Engn Aswan Egypt Univ Tunis Dept Elect Engn Tunis Tunisia

ISBN: (纸本)9781728152899

This paper proposes a combined analytical and metaheuristic optimization algorithm for optimal allocation of multiple types of Distributed Generators (DGs) in radial distribution networks to minimize the total power losses. The analytical technique based on the exact loss formula is used to calculate the optimal size of the DG unit at a certain bus. However, the analytical technique may not be suitable to allocate multiple DGs types due to the mass of calculations, hence, lightning search algorithm (LSA) is combined with the analytical technique to find the optimal locations. Like any random search-based optimization algorithm, LSA faces some issues such as low convergence rate and trapping in local solutions. So, to avoid these kinds of problems, a proposed Chaotic LSA (CLSA) based on Gauss/mouse map is established. The gauss/mouse map is used to change random LSA forking occurrence instead of using the random variable. The proposed Combined Analytical CLSA (CA-CLSA) is validated using the stranded radial distribution feeders such as 15 and 30-networks. A comprehensive comparison between the proposed technique and other competitive optimization techniques is carried out to prove its effectiveness. The result shows that the proposed combined analytical CA-CLSA is efficient to allocate the multiple DGs types with minimum power loss and high convergence rate.

关键词： Analytical technique lightning search algorithm Optimal size and location Power loss

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Internet of things multi hop energy efficient cluster-based routing using particle swarm optimization

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WIRELESS NETWORKS 2021年第8期27卷 5207-5215页

作者： Senthil, G. A. Raaza, Arun Kumar, N. Vels Inst Sci Technol & Adv Studies VISTAS Dept Elect & Commun Engn Chennai Tamil Nadu India Vels Inst Sci Technol & Adv Studies VISTAS Dept Comp Sci & Engn Chennai Tamil Nadu India

The Internet of Things (IoT) is a collection of various sensors connected to the internet that share information. In a large-scale IoT network, data is collected through the wireless sensor network (WSN), and the aggregated data is sent from the sink to the next level of IoT for processing. Clustering is utilized to cut down on energy use, network redundancy, interference, and collision in WSN and improve network lifetime, scalability, and data aggregation. In addition, multi-hop communication is more effective for networks with sensors that cover a broad region. The Multi-Hop Low Energy Adaptive Clustering Hierarchy brings about a reduction to the transmission distance and prolongs the network lifetime. This particle swarm optimization (PSO) technique is effective for determining the most effective solutions for a particular problem. The particles in the PSO embody the candidate solutions tend to move through their solutions space (in several directions) in different velocities. A distributed multi-hop cluster-based routing algorithm that takes advantage of the PSO and the Lightening search algorithm is developed in this work. The proposed method optimizes the clustering process and achieves energy efficiency, as demonstrated by the experimental results. Reduced end-to-end delay and lower packet loss rate whereas the lifespan network and cluster count are improved.

关键词： IoT Wireless sensor network Clustering Multi-hop low energy adaptive clustering hierarchy Particle swarm optimization lightning search algorithm

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Study on Multi-UAV Cooperative Path Planning for Complex Patrol Tasks in Large Cities

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DRONES 2023年第6期7卷 367-367页

作者： Xiang, Hongyu Han, Yuhang Pan, Nan Zhang, Miaohan Wang, Zhenwei Kunming Univ Sci & Technol Fac Civil Aviat & Aeronaut Kunming 650500 Peoples R China

Unmanned Aerial Vehicles (UAVs) are increasingly utilized for urban patrol and defense owing to their low cost, high mobility, and rapid deployment. This paper proposes a multi-UAV mission planning model that takes into account mission execution rates, flight energy consumption costs, and impact costs. A kinematics and dynamics model of a quadcopter UAV is established, and the UAV's flight state is analyzed. Due to the difficulties in addressing 3D UAV kinematic constraints and poor uniformity using traditional optimization algorithms, a lightning search algorithm (LSA) based on multi-layer nesting and random walk strategies (MNRW-LSA) is proposed. The convergence performance of the MNRW-LSA algorithm is demonstrated by comparing it with several other algorithms, such as the Golden Jackal Optimization (GJO), Hunter-Prey Optimization (HPO), Pelican Optimization algorithm (POA), Reptile search algorithm (RSA), and the Golden Eagle Optimization (GEO) using optimization test functions, Friedman and Nemenyi tests. Additionally, a greedy strategy is added to the Rapidly-Exploring Random Tree (RRT) algorithm to initialize the trajectories for simulation experiments using a 3D city model. The results indicate that the proposed algorithm can enhance global convergence and robustness, shorten convergence time, improve UAV execution coverage, and reduce energy consumption. Compared with other algorithms, such as Particle Swarm Optimization (PSO), Simulated Annealing (SA), and LSA, the proposed method has greater advantages in addressing multi-UAV trajectory planning problems.

关键词： lightning search algorithm multi-layer nesting strategy path planning quadcopter UAV urban patrol

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Cuttlefish algorithm-Based Multilevel 3-D Otsu Function for Color Image Segmentation

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IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2020年第5期69卷 1871-1880页

作者： Bhandari, Ashish Kumar Kumar, Immadisetty Vinod Srinivas, Kankanala Natl Inst Technol Patna Dept Elect & Commun Engn Patna 800005 Bihar India

To overcome the shortcomings of 1-D and 2-D Otsu's thresholding methods, a 3-D Otsu method has been introduced. While yielding satisfactory segmentation results for images with a low signal-to-noise ratio (SNR) and poor contrast, it has the downside of high computational complexity. In this paper, the cuttlefish algorithm (CFA)-based 3-D Otsu thresholding method is proposed to pace up the conventional 3-D Otsu thresholding for color image segmentation. In order to decrease the effects of noises and weak edges, an optimally selected multilevel 3-D Otsu image thresholding technique is brought into the proposed segmentation scheme. The CFA is a newly developed stochastic meta-heuristic optimization algorithm based on observing, mimicking, and modeling the camouflaging feature of cuttlefish. It is used to simplify the problem of exhaustive search for the optimal threshold vector in 3-D space. Experimental results, when compared to 1-D Otsu, 1-D Otsu-Cuckoo search (CS) algorithm, 1-D Otsu-lightning search algorithm (LSA), 1-D Otsu-CFA, conventional 3-D Otsu, 3-D Otsu-CS, and 3-D Otsu-LSA, indicate that the proposed algorithm CFA-based 3-D Otsu thresholding is superior to all the other multilevel thresholding algorithms. The proposed 3-D-CFA method produces promising segmentation results from the objective and subjective aspects.

关键词： 3-D Otsu color image segmentation Cuckoo search (CS) algorithm cuttlefish algorithm (CFA) lightning search algorithm multilevel thresholding

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