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

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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Enhanced seagull optimization algorithm for Photovoltaic Cell Parameter Estimating 41

Enhanced Seagull Optimization Algorithm for Photovoltaic Cel...

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第41届中国控制会议

作者： Hongyin Wei Kailin Xu Jianming Zhang Institute of Cyber-Systems and Control State Key Laboratory of Industrial Control TechnologyZhejiang University

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

In this paper,an enhanced seagull optimization algorithm based on a double group evolutionary mechanism(DSOA) is proposed for photovoltaic(PV) cell parameter *** the DSOA,a double group search strategy and time-based mutation strategy are added to the population renewal mechanism of the conventional *** performance of DSOA on standard benchmark functions is surprising;it was good at accuracy and exploitation,also convergence *** with SOA and the other two classical intelligent optimization algorithms,the performance of DSOA is comprehensively *** is adopted to estimate PV cell *** simulation results demonstrate that estimated currents of circuit model based on DSOA have high similarity to the measured value of the photovoltaic cell.

关键词： Photovoltaic(PV) parameter estimating seagull optimization algorithm intelligent optimization algorithms

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Improved seagull optimization algorithm Incorporating Golden Sine and Tent Chaotic Perturbations 6

Improved Seagull Optimization Algorithm Incorporating Golden...

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6th IEEE Advanced Information Technology, Electronic and Automation Control Conference (IEEE IAEAC)

作者： Chen, Ange Peng, Hanzhang Zhong, Yu Ren, Huimin China Acad Launch Vehicle Technol Beijing Aerosp Automat Control Inst Beijing Peoples R China Natl Key Lab Sci & Technol Aerosp Intelligence Co Beijing Peoples R China

ISBN: (数字)9781665458641

ISBN: (纸本)9781665458641

Aiming at the defects of slow convergence and easy to fall into local optimum of the seagull optimization algorithm, this paper proposes an improved seagull optimization algorithm incorporating golden sine and chaotic perturbation of tent mapping. This algorithm enhances the global search ability through tent chaotic disturbance and Levy flight, accelerates the convergence through golden sine to improve the local search ability. In this paper, the original fixed convergence factor is transformed into a nonlinear decreasing convergence factor to improve the optimization efficiency. The performance is tested on the benchmark functions, and it is used to solving the multiprocessor task scheduling problem. Compared with other algorithms, experiments show that TGSOA has significant improvement over other algorithms in terms of convergence speed and robustness.

关键词： seagull optimization algorithm Golden sine Tent Mapping Levy flight

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Improved seagull optimization algorithm Based on Multi-Strategy Integration 34

Improved Seagull Optimization Algorithm Based on Multi-Strat...

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34th Chinese Control and Decision Conference (CCDC)

作者： Shi, Haibin Li, Baoda Northeastern Univ Coll Informat Sci & Engn Shenyang 110004 Peoples R China

ISBN: (纸本)9781665478960

Aiming at the defects of seagull optimization algorithm (SOA) in solving optimization problems, such as local optimization, slow convergence speed and low optimization accuracy, a seagull optimization algorithm (SCSOA) based on integration of improved Sobol sequence and Cauchy variation is proposed. First, initialize the population using the Sobol sequence to make the seagulls more evenly distributed in the initial solution space;Secondly, use a nonlinear function to replace the original convergence factor to guide the seagull to always maintain a larger body degree of freedom during the search process, enhance the global search ability, and avoid falling into the local optimum;Then, the Cauchy variation strategy is introduced, so that the individual can better find the optimal solution and enhance the ability of the algorithm to jump out of the local optimum;Finally, use the benchmark function to test the improved algorithm, and compare it with the original algorithm and the experimental results of other algorithms. The results show that SCSOA performs better in convergence speed and optimization accuracy, and the global optimization capability is also improved.

关键词： seagull optimization algorithm Sobol Sequence Cauchy Variation

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EMoSOA: a new evolutionary multi-objective seagull optimization algorithm for global optimization

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INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS 2021年第2期12卷 571-596页

作者： Dhiman, Gaurav Singh, Krishna Kant Slowik, Adam Chang, Victor Yildiz, Ali Riza Kaur, Amandeep Garg, Meenakshi Govt Bikram Coll Commerce Dept Comp Sci Patiala 147001 Punjab India Delhi NCR Dept Elect & Commun Engn KIET Grp Inst Ghaziabad India Koszalin Univ Technol Dept Elect & Comp Sci Sniadeckich 2 PL-75453 Koszalin Poland Teesside Univ Sch Comp Engn & Digital Technol Middlesbrough Cleveland England Uludag Univ Dept Automot Engn Coll Engn TR-16059 Bursa Turkey Sri Guru Granth Sahib World Univ Dept Comp Sci & Engn Fatehgarh Sahib Punjab India

This study introduces the evolutionary multi-objective version of seagull optimization algorithm (SOA), entitled Evolutionary Multi-objective seagull optimization algorithm (EMoSOA). In this algorithm, a dynamic archive concept, grid mechanism, leader selection, and genetic operators are employed with the capability to cache the solutions from the non-dominatedPareto. The roulette-wheel method is employed to find the appropriate archived solutions. The proposed algorithm is tested and compared with state-of-the-art metaheuristic algorithms over twenty-four standard benchmark test functions. Four real-world engineering design problems are validated using proposedEMoSOAalgorithm to determine its adequacy. The findings of empirical research indicate that the proposed algorithm is better than other algorithms. It also takes into account those optimal solutions from theParetowhich shows high convergence.

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

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Optimal operation of multi-reservoir systems for increasing power generation using a seagull optimization algorithm and heading policy

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ENERGY REPORTS 2021年 7卷 3703-3725页

作者： Ehteram, Mohammad Banadkooki, Fatemeh Barzegari Fai, Chow Ming Moslemzadeh, Mohsen Sapitang, Michelle Ahmed, Ali Najah Irwan, Dani El-Shafie, Ahmed Semnan Univ Fac Civil Engn Dept Water Engn & Hydraul Struct Semnan Iran Payame Noor Univ Agr Dept Tehran Iran Univ Tenaga Nasl UNITEN Coll Engn Dept Civil Engn Kajang 43000 Selangor Malaysia Monash Univ Malaysia Sch Engn Discipline Civil Engn Jalan Lagoon Selatan Bandar Sunway 47500 Selangor Malaysia Univ Tenaga Nasl UNITEN Coll Engn Inst Energy Infrastruct IEI Dept Civil Engn Kajang 43000 Selangor Malaysia Int Islamic Univ Malaysia Civil Engn Dept Kulliyyah Engn Kuala Lumpur Malaysia Univ Malaya UM Fac Engn Dept Civil Engn Kuala Lumpur 50603 Malaysia United Arab Emirates Univ Natl Water & Energy Ctr POB 15551 Al Ain U Arab Emirates

Power supply is a key issue for decision-makers. The reservoir operation of multi-reservoir systems is an important aspect to consider in efforts to increase power generation. This research studies a multi reservoir system comprising of the Khersan-I (KHI), Karoon-III (KAIII) and Karoon-IV (KAIV) with the intent being to increase power generation. To achieve this, the Two-Point Heading Rule was integrated with a new optimization algorithm, namely the seagull optimization algorithm (SEOA). The Two Point Heading Rule was used based on four distinct scenarios, namely Two-Point Heading Rule (1), Two-Point Heading Rule (2), Two-Point Heading Rule (3) and Two-Point Heading Rule (4). The seagull optimization algorithm was then used to find two heading parameters of the TPHRs. The seagull optimization algorithm was subsequently benchmarked against the Salp Swarm algorithm (SSA), Bat algorithm (BA) and the Shark optimization algorithm (SOA). Various inflow scenarios consisting of the first inflow scenario (dry condition), the second inflow scenario (normal) and the third inflow scenario (wet condition) were considered for the optimal operation of this multi-reservoir system. The results indicated that the global solution of the MSOO based on NLP for Two-Point Heading Rule (1) under the first inflow scenario and was 3.22 while the average solution of seagull optimization algorithm, Salp Swarm algorithm, Shark optimization algorithm, and Bat algorithm in respective order was 3.25, 3.93, 4.87 and 6.03. The results indicated that the global solution of the MSOO based on NLP for Two-Point Heading Rule (1) under the second inflow scenario was 2.14 while the average best solution of seagull optimization algorithm, Salp Swarm algorithm, Shark optimization algorithm, and Bat algorithm in respective order was 2.16, 2.98, 3.96, and 4.89. It can be concluded that the SEOA outperformed all of the other algorithms. It was also found that the SEOA based on the Two-Point Heading Rule (3) un

关键词： seagull optimization algorithm Multi-reservoir operation Heading policy Power generation

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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-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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Research on the seagull optimization algorithm-based convolutional neural network rolling bearing fault diagnosis method

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ENGINEERING RESEARCH EXPRESS 2023年第3期5卷 035050-035050页

作者： Xue, Jijun Liu, Xiaodong Xu, Hao Zhang, Di Xian Shiyou Univ Mech Engn Coll Xian 710065 Shaanxi Peoples R China

It is crucial to understand the rolling bearing fault diagnosis procedure since rolling bearings are frequently used in rotating machinery and if a failure occurs, it will interfere with the proper operation of the entire piece of machinery or piece of equipment. Deep learning is increasingly being used in mechanical fault diagnosis, with convolutional neural networks(CNN) being the most common type. In recent years, the rapid growth of artificial intelligence has caused fault diagnosis methods to evolve as well. A CNN fault diagnostic approach based on seagull optimization algorithm (SOA) is suggested and applied to the fault diagnosis of rolling bearings in order to address the issues with convolutional neural networks, such as high data requirements, unstable gradients, and optimization challenges. The seagull optimization approach is used to design a deep learning model by selecting the structural hyperparameters in the CNN model as efficiently as possible. The accelerated life experimental dataset for rolling bearings is trained using the optimized CNN model. The training outcomes of the CNN model on the dataset before and after optimization are compared, and the findings demonstrate that the suggested method has a superior optimization effect. As can be seen, the seagull optimization algorithm and CNN can be organically combined and applied to the fault diagnosis method for rolling bearings.

关键词： rolling bearing fault diagnosis convolutional neural network seagull optimization algorithm

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Application of Improved seagull optimization algorithm on Optimal Allocation optimizations of Distributed Generation

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ENGINEERING LETTERS 2023年第3期31卷 1页

作者： Qian, Jie Peng, Yuhan Zheng, Haoling Wang, Xi Chongqing Univ Posts & Telecommun Chongqing 400065 Peoples R China State Grid Chongqing Elect Power Co Bishan Power Supply Branch Chongqing 402760 Peoples R China State Grid Sichuan Elect Power Co Leshan Power Supply Co Chongqing 614000 Sichuan Peoples R China

Optimal allocation optimization (OAO) research is an important technology to construct new distribution networks (DNs) with environmental protection and high efficiency. The OAO research in this paper aims to achieve the more ideal operation of DNs by exploring superior allocation schemes including the optimal power factor, access node and capacity of distributed generations (DGs). In order to solve the complex OAO problems, an improved seagull optimization algorithm with elite reserve (ISOAE) is proposed and applied to two different OAO scenarios. In traditional OAO cases with constant loads, the suggested ISOAE finds better DG allocation schemes with less power loss than the basic seagull optimization algorithm (BSOA) and multiple published algorithms. Simulation experiments show that the number, location, capacity and power factor of DGs all have a non-negligible impact on the operation of DNs. In OAO cases with practical seasonal loads, the access of traditional generator (CG), renewable distributed generators represented by photovoltaic (PV) and wind turbine (WT) into the DNs is studied. Multiple experiments on the IEEE 33-bus network demonstrate that CG achieves the relatively best operation status than PV and WT. However, considering resource conservation and environmental protection, renewable PV and WT are more favored by actual power grids. Furthermore, WT operating at the optimal power factor is superior to the PV with uneven radiation intensity distribution in reducing daily active energy loss and increasing node voltage of DNs. In general, the reasonable integration of renewable energy such as WT is of great significance for achieving the two-carbon strategy of power grids.

关键词： seagull optimization algorithm Renewable energy Optimal allocation Wind turbine

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