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 optimizati...
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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.
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 ...
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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 optimizationalgorithms,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.
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...
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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.
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...
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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.
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 archi...
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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.
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 s...
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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 optimizationalgorithm, 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 optimizationalgorithm (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 optimizationalgorithm, 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 optimizationalgorithm, 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
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 t...
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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 optimizationalgorithm 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.
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 rendere...
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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 optimizationalgorithm (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.
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 en...
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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 seagulloptimization 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.
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 ...
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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.
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