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Parameters estimation of photovoltaic models using a novel hybrid seagull optimization algorithm

ENERGY 2022年 249卷 123760-123760页

作者： Long, Wen Jiao, Jianjun Liang, Ximing Xu, Ming Tang, Mingzhu Cai, Shaohong Guizhou Univ Finance & Econ Key Lab Econ Syst Simulat Guiyang 550025 Peoples R China Guizhou Univ Finance & Econ Sch Math & Stat Guiyang 550025 Peoples R China Beijing Univ Civil Engn & Architecture Sch Sci Beijing 100044 Peoples R China Changsha Univ Sci & Technol Sch Energy Power & Engn Changsha 410114 Peoples R China

Estimating parameters and establishing high-accuracy and high-reliability models of photovoltaic (PV) modules by using the actual current-voltage data is important to simulate, model, and optimize the PV systems. Several meta-heuristic optimization techniques have been developed to estimate the parameters of the solar PV models. However, it is still a challenging task to accurately, reliably, and quickly estimate the unknown parameters of PV models. This paper proposes a novel hybrid seagull optimization algorithm (HSOA) for estimating the unknown parameters of PV models effectively and accurately. In proposed HSOA, the personal historical best information is embedded into position search equation to improve the solution precision. A novel nonlinear escaping energy factor based on cosine function is presented for balancing global exploration and local exploitation. The differential mutation strategy is introduced to escape from the local optima. We firstly select twelve classical benchmark test functions to investigate the feasibility of HSOA, and experimental results show that HSOA is superior to most compared methods. Then, HSOA is used for solving parameters estimation problem of three benchmark solar PV models. The comparison results demonstrate that HSOA is superior to BOA, GWO, WOA, HHO, SOA, EEGWO, and ISCA on solution quality, convergence and reliability.(c) 2022 Elsevier Ltd. All rights reserved.

关键词： seagull optimization algorithm Photovoltaic models Parameter estimation Differential evolution Function optimization

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optimization approach of berth-quay crane-truck allocation by the tide, environment and uncertainty factors based on chaos quantum adaptive seagull optimization algorithm

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APPLIED SOFT COMPUTING 2024年 152卷

作者： Li, Ming-Wei Xu, Rui-Zhe Yang, Zhong-Yi Hong, Wei-Chiang An, Xiao-Gang Yeh, Yi-Hsuan Harbin Engn Univ Coll Shipbuilding Engn Harbin 150001 Peoples R China Harbin Engn Univ Nanhai Inst Sanya 572024 Hainan Peoples R China Harbin Engn Univ Sch Econ & Management Harbin 150001 Peoples R China Asia Eastern Univ Sci & Technol Dept Informat Management New Taipei 22046 Taiwan Yuan Ze Univ Dept Informat Management Zhongli 320315 Taiwan China Waterborne Transport Res Inst Beijing 100088 Peoples R China

The post-epidemic era has led to the accumulation of cargo, which has brought greater pressure to container ports. Since traditional methods cannot simultaneously consider the effect of tidal, uncertain, and environmental factors on the allocation plan. To relieve this pressure, firstly, considering tidal factors, formulating time window rules, thinking out uncertain factors, and determining constraints from three perspectives of vessel berthing process, quay crane and container truck operation, a new berth-quay crane-truck joint scheduling model is constructed by minimizing three aspects of vessels turnaround time, the carbon emissions of quay cranes and trucks, namely TEU-BQCT model. Then, aiming at obtaining a relatively high-quality solution, combining chaotic mapping and quantum entanglement, a new chaotic quantum adaptive seagull optimization algorithm is proposed, namely CQASOA, exclusive coding rules suitable for the TEU-BQCT model is formulated, a feasible integer algorithm is developed, the external penalty function is constructed to limit constraints, and a novel joint scheduling solution method of berth-quay crane-truck is proposed, namely TEU-BQCT_CQASOA. Subsequently, two ports of different scales in South China are used to test the constructed solution method feasibility. The simulation results indicate that the constructed TEU-BQCT model can obtain a more suitable scheduling scheme. The proposed CQASOA has better performance than other comparison algorithms selected in this paper, which can obtain a better solution when solving the TEU-BQCT model.

关键词： Berth-quay crane-truck joint scheduling Tidal factor Uncertainty factor seagull optimization algorithm External penalty function method Quantum computing Chaotic mapping

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An improved seagull optimization algorithm for optimal coordination of distance and directional over-current relays

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EXPERT SYSTEMS WITH APPLICATIONS 2022年第0期200卷 1页

作者： Abdelhamid, Mohamed Houssein, Essam H. Mahdy, Mohamed A. Selim, Ali Kamel, Salah Aswan Univ Fac Engn Elect Engn Dept Aswan Egypt Minia Univ Fac Comp & Informat Al Minya Egypt Beni Suef Univ Fac Comp & Artificial Intelligence Bani Suwayf Egypt

In this paper, an improved bio-inspired optimization algorithm called seagull optimization algorithm (ISOA) is proposed. ISOA is developed based on three techniques, these techniques use the collaborative optimization strategy, simulating new bird's generation, and rearranging subgroups. The CEC2020 is utilized to obtain the role and importance of the improved algorithm. Furthermore, the ISOA is applied to find the optimal coordi-nation of Distance and Directional Over-Current Relays (D&DOCRs). The problem of D&DOCRs coordination is more complicated and has numerous constraints. Hence, in this work, a developed object function (DOF) is proposed to set the Distance relays with minimum available settings. ISOA and DOF are tested on the IEEE 8-bus and IEEE 14-bus test systems. ISOA algorithm is used to coordinate the D&DOCRs in both the near and far ends of transmission lines. The results of test systems prove the effectiveness of ISOA to solve the coordination of D&DOCRs using the DOF.

关键词： seagull optimization algorithm Improved seagull optimization algorithm Optimal relay coordination Distance relay Directional over-current relay

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Multi-strategy Improved seagull optimization algorithm

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INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE SYSTEMS 2023年第1期16卷 154-154页

作者： Li, Yancang Li, Weizhi Yuan, Qiuyu Shi, Huawang Han, Muxuan Hebei Univ Engn Coll Civil Engn Handan 056038 Peoples R China Tianjin Univ Sch Civil Engn Tianjin 300354 Peoples R China

Aiming at the shortcomings of seagull optimization algorithm in the process of searching for optimization, such as slow convergence speed, low precision, easy falling into local optimal, and performance dependent on the selection of parameters, this paper proposes an improved gull optimization algorithm based on multi-strategy fusion based on the analysis of gull population characteristics. Firstly, L-C cascade chaotic mapping is used to initialize the population so that seagulls are more evenly distributed in the initial solution space. Secondly, to improve the algorithm's global exploration ability in the early stage, the nonlinear convergence factor is incorporated to adjust the position of seagulls in the migration stage. At the same time, the group learning strategy was introduced after the population position update to improve the population quality and optimization accuracy further. Finally, in the late stage of the algorithm, the golden sine strategy of the Levy flight guidance mechanism is used to update the population position to improve the population's diversity and enhance the local development ability of the algorithm in the late stage. To verify the optimization performance of the improved algorithm, CEC2017 and CEC2022 test suites are selected for simulation experiments, and box graphs are drawn. The test results show that the proposed algorithm has apparent convergence speed, accuracy, and stability advantages. The engineering case results demonstrate the proposed algorithm's advantages in solving complex problems with unknown search spaces.

关键词： seagull optimization algorithm algorithm improvement Simulation experiment Structural optimization

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Multi-strategy improved seagull optimization algorithm and its application in practical engineering

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ENGINEERING optimization 2024年

作者： Chen, Peng Li, Huilin He, Feng Bian, Dongsheng Guizhou Univ Dept Mech Engn Guiyang Peoples R China Chery Wanda Guizhou Bus Co Ltd Guiyang Peoples R China

Metaheuristic algorithms play a crucial role in engineering optimization, as they can find the optimal parameter configuration in engineering systems. This article proposes a multi-strategy improved seagull optimization algorithm (OPSOA) to solve engineering application problems. Aiming to solve the problems of slow search speed and low convergence accuracy of the standard seagull optimization algorithm (SOA), four strategies, including L & eacute;vy flight and Cauchy mutation, were introduced to improve its performance. Comparison shows that OPSOA and its incomplete algorithms are better than SOA, indicating that each improvement is effective. By testing the benchmark functions of CEC 2017 and CEC 2022, it is shown that OPSOA has a strong ability to find the optimal solution and is superior to other algorithms in terms of convergence accuracy and search speed. The application of this algorithm in practical engineering problems proves that it has significant advantages in solving complex problems.

关键词： seagull optimization algorithm algorithm improvement experimental analysis engineering application

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MOSOA: A new multi-objective seagull optimization algorithm

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EXPERT SYSTEMS WITH APPLICATIONS 2021年 167卷 114150-114150页

作者： Dhiman, Gaurav Singh, Krishna Kant Soni, Mukesh Nagar, Atulya Dehghani, Mohammad Slowik, Adam Kaur, Amandeep Sharma, Ashutosh Houssein, Essam H. Cengiz, Korhan Govt Bikram Coll Commerce Dept Comp Sci Patiala 147001 Punjab India KIET Grp Inst Dept Elect & Commun Engn Delhi Ghaziabad India Smt SR Patel Engn Coll Dept Comp Engn Unjha 384170 Gujarat India Liverpool Hope Univ Res Hope Pk Liverpool L16 9JD Merseyside England Shiraz Univ Technol Dept Elect & Elect Engn Shiraz Iran Koszalin Univ Technol Dept Elect & Comp Sci Sniadeckich 2 PL-75453 Koszalin Poland Sri Guru Granth Sahib Wrold Univ Dept Comp Sci Fatehgarh Sahib Punjab India Lovely Profess Univ Sch Elect & Elect Engn Phagwara Punjab India Menia Univ Fac Comp & Informat Al Minya 61519 Egypt Trakya Univ Dept Elect & Elect Engn TR-22030 Edirne Turkey

This study introduces the extension of currently developed seagull optimization algorithm (SOA) in terms of multi-objective problems, which is entitled as Multi-objective seagull optimization algorithm (MOSOA). In this algorithm, a concept of dynamic archive is introduced, which has the feature to cache the non-dominated Pareto optimal solutions. The roulette wheel selection approach is utilized to choose the effective archived solutions by simulating the migration and attacking behaviors of seagulls. The proposed algorithm is approved by testing it with twenty-four benchmark test functions, and its performance is compared with existing metaheuristic algorithms. The developed algorithm is analyzed on six constrained problems of engineering design to assess its appropriateness for finding the solutions of real-world problems. The outcomes from the empirical analyzes depict that the proposed algorithm is better than other existing algorithms. The proposed algorithm also considers those Pareto optimal solutions, which demonstrate high convergence.

关键词： Convergence Diversity Pareto Solutions Multi-objective optimization seagull optimization algorithm Engineering Design Problems

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Collision-Aware Routing Using Multi-Objective seagull optimization algorithm for WSN-Based IoT

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SENSORS 2021年第24期21卷 8496-8496页

作者： Jagannathan, Preetha Gurumoorthy, Sasikumar Stateczny, Andrzej Divakarachar, Parameshachari Bidare Sengupta, Jewel Muthayammal Engn Coll Autonomous Dept Comp Sci & Engn Rasipuram 637408 Tamil Nadu India Jerusalem Coll Engn Dept Comp Sci & Engn Chennai 600100 Tamil Nadu India Gdansk Univ Technol Dept Geodesy PL-80233 Gdansk Poland GSSS Inst Engn & Technol Women Dept Telecommun Engn Mysuru 570016 Karnataka India Kaunas Univ Technol Dept Informat Engn LT-44249 Kaunas Lithuania

In recent trends, wireless sensor networks (WSNs) have become popular because of their cost, simple structure, reliability, and developments in the communication field. The Internet of Things (IoT) refers to the interconnection of everyday objects and sharing of information through the Internet. Congestion in networks leads to transmission delays and packet loss and causes wastage of time and energy on recovery. The routing protocols are adaptive to the congestion status of the network, which can greatly improve the network performance. In this research, collision-aware routing using the multi-objective seagull optimization algorithm (CAR-MOSOA) is designed to meet the efficiency of a scalable WSN. The proposed protocol exploits the clustering process to choose cluster heads to transfer the data from source to endpoint, thus forming a scalable network, and improves the performance of the CAR-MOSOA protocol. The proposed CAR-MOSOA is simulated and examined using the NS-2.34 simulator due to its modularity and inexpensiveness. The results of the CAR-MOSOA are comprehensively investigated with existing algorithms such as fully distributed energy-aware multi-level (FDEAM) routing, energy-efficient optimal multi-path routing protocol (EOMR), tunicate swarm grey wolf optimization (TSGWO), and CoAP simple congestion control/advanced (CoCoA). The simulation results of the proposed CAR-MOSOA for 400 nodes are as follows: energy consumption, 33 J;end-to-end delay, 29 s;packet delivery ratio, 95%;and network lifetime, 973 s, which are improved compared to the FDEAM, EOMR, TSGWO, and CoCoA.

关键词： congestion Internet of Things scalability seagull optimization algorithm wireless sensor network

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Multi-Objective seagull optimization algorithm with Deep Learning-Enabled Vulnerability Detection for Secure Cloud Environments

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SENSORS 2023年第23期23卷 9383页

作者： Aljebreen, Mohammed Alohali, Manal Abdullah Mahgoub, Hany Aljameel, Sumayh S. Alsumayt, Albandari Sayed, Ahmed King Saud Univ Community Coll Dept Comp Sci POB 28095 Riyadh 11437 Saudi Arabia Princess Nourah Bint Abdulrahman Univ Coll Comp & Informat Sci Dept Informat Syst POB 84428 Riyadh 11671 Saudi Arabia King Khalid Univ Coll Sci & Art Mahayil Dept Comp Sci Abha 61413 Saudi Arabia Imam Abdulrahman Bin Faisal Univ Coll Comp Sci & Informat Technol SAUDI ARAMCO Cybersecur Chair Dept Comp Sci POB 1982 Dammam 31441 Saudi Arabia Imam Abdulrahman Bin Faisal Univ Appl Coll Dept Comp Sci POB 1982 Dammam 31441 Saudi Arabia Future Univ Egypt Res Ctr New Cairo 11835 Egypt

Cloud computing (CC) is an internet-enabled environment that provides computing services such as networking, databases, and servers to clients and organizations in a cost-effective manner. Despite the benefits rendered by CC, its security remains a prominent concern to overcome. An intrusion detection system (IDS) is generally used to detect both normal and anomalous behavior in networks. The design of IDS using a machine learning (ML) technique comprises a series of methods that can learn patterns from data and forecast the outcomes consequently. In this background, the current study designs a novel multi-objective seagull optimization algorithm with a deep learning-enabled vulnerability detection (MOSOA-DLVD) technique to secure the cloud platform. The MOSOA-DLVD technique uses the feature selection (FS) method and hyperparameter tuning strategy to identify the presence of vulnerabilities or attacks in the cloud infrastructure. Primarily, the FS method is implemented using the MOSOA technique. Furthermore, the MOSOA-DLVD technique uses a deep belief network (DBN) method for intrusion detection and its classification. In order to improve the detection outcomes of the DBN algorithm, the sooty tern optimization algorithm (STOA) is applied for the hyperparameter tuning process. The performance of the proposed MOSOA-DLVD system was validated with extensive simulations upon a benchmark IDS dataset. The improved intrusion detection results of the MOSOA-DLVD approach with a maximum accuracy of 99.34% establish the proficiency of the model compared with recent methods.

关键词： cloud computing deep learning intrusion detection system sooty tern optimization algorithm seagull optimization algorithm

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SOA-EACR: seagull optimization algorithm based energy aware cluster routing protocol for wireless sensor networks in the livestock industry

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SUSTAINABLE COMPUTING-INFORMATICS & SYSTEMS 2022年第0期33卷 100645-100645页

作者： Sankar, S. Somula, Ramasubbareddy Parvathala, Balakesavareddy Kolli, Srinivas Pulipati, Srilatha Srinivas, Aditya Sai T. Sona Coll Technol Dept Comp Sci & Engn Salem India VNRVJIET Dept Informat Technol Hyderabad India Anurag Univ Dept Comp Sci & Engn Hyderabad India G Pullaiah Coll Engn & Technol Comp Sci & Engn Kurnool Andhra Pradesh India

The primary goal of Wireless Sensor Networks (WSNs) in the livestock industry is to monitor the health conditions of various animals and their activities. Energy conservation in WSN is one of the most challenging tasks since the devices are connected to limited resource devices. So, clustering is the best choice to save energy in WSN. The sensor nodes are connected to the animal and move from one location to another because of the dynamic nature. In recent years, several clustering protocols have been proposed using an optimization algorithm to conserve energy in the network. However, it takes more time to converge in selecting the best cluster head in the network. The energy is therefore drained early in the network. To overcome the limitation, this paper proposes a seagull optimization algorithm based Energy Aware Cluster Routing Protocol (SOA-EACR) in WSN to prolong the network's lifespan and reduce the end to end delay. The SOA-EACR balances the exploration and exploitation during the CH selection. Thus, it prolongs the lifespan of the network. The simulation is performed using MATLAB 2019a. The efficacy of SOA-EACR is compared with CLPSO, CACONET and GWO. The proposed SOA-EACR enhances the network life by 20 % and increases the packet delivery ratio by 2-5 %.

关键词： seagull optimization algorithm Clustering protocol Wireless sensor networks Cluster head

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Wind Power Forecasting Using Optimized Dendritic Neural Model Based on seagull optimization algorithm and Aquila Optimizer

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ENERGIES 2022年第24期15卷 9261页

作者： Al-qaness, Mohammed A. A. Ewees, Ahmed A. A. Abd Elaziz, Mohamed Samak, Ahmed H. H. Zhejiang Normal Univ Coll Phys & Elect Informat Engn Jinhua 321004 Peoples R China Univ Bisha Coll Comp & Informat Technol Bisha 61922 Saudi Arabia Damietta Univ Dept Comp Dumyat 34517 Egypt Zagazig Univ Fac Sci Dept Math Zagazig 44519 Egypt Galala Univ Fac Comp Sci & Engn Suze 435611 Egypt Ajman Univ Artificial Intelligence Res Ctr AIRC Ajman 346 U Arab Emirates Lebanese Amer Univ Dept Elect & Comp Engn Byblos 13518 Lebanon Menoufia Univ Fac Sci Shibin Al Kawm 32511 Egypt

It is necessary to study different aspects of renewable energy generation, including wind energy. Wind power is one of the most important green and renewable energy resources. The estimation of wind energy generation is a critical task that has received wide attention in recent years. Different machine learning models have been developed for this task. In this paper, we present an efficient forecasting model using naturally inspired optimization algorithms. We present an optimized dendritic neural regression (DNR) model for wind energy prediction. A new variant of the seagull optimization algorithm (SOA) is developed using the search operators of the Aquila optimizer (AO). The main idea is to apply the operators of the AO as a local search in the traditional SOA, which boosts the SOA's search capability. The new method, called SOAAO, is employed to train and optimize the DNR parameters. We used four wind speed datasets to assess the performance of the presented time-series prediction model, called DNR-SOAAO, using different performance indicators. We also assessed the quality of the SOAAO with extensive comparisons to the original versions of the SOA and AO, as well as several other optimization methods. The developed model achieved excellent results in the evaluation. For example, the SOAAO achieved high R2 results of 0.95, 0.96, 0.95, and 0.91 on the four datasets.

关键词： wind power dendritic neural regression (DNR) seagull optimization algorithm Aquila optimizer metaheuristic time series forecasting

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