检索结果-内蒙古大学图书馆

An Improved Multi-Objective Particle swarm Optimization With TOPSIS and Fuzzy Logic for Optimizing Trapezoidal Labyrinth Weir

引用

IEEE ACCESS 2021年 9卷 25458-25472页

作者： Mahmoud, Ali Yuan, Xiaohui Kheimi, Marwan Almadani, Mohammad A. Hajilounezhad, Taher Yuan, Yanbin Huazhong Univ Sci & Technol Sch Civil & Hydraul Engn Wuhan 430074 Peoples R China China Three Gorges Univ Hubei Prov Key Lab Operat & Control Cascaded Hydr Yichang 443002 Peoples R China King Abdulaziz Univ Fac Engn Dept Civil & Environm Engn Rabigh Branch Jeddah 21589 Saudi Arabia Univ Missouri Dept Mech & Aerosp Engn Columbia MO 65211 USA Wuhan Univ Technol Sch Resources & Environm Engn Wuhan 430070 Peoples R China

Labyrinth Weir (LW) is a popular control structure that passes a significantly higher flow rate compared to the linear weirs. In order to approach the optimal design of a trapezoidal LW, a multi-objective problem is defined to concurrently minimize the LW consumed concrete volume and maximize its discharge capacity. Simultaneously, a Radial Basis function Neural Networks (RBFNN) is designed and used for estimating LW discharge coefficient (C-d) according to the existing experimental results. An improved multi-objective particle swarm optimization (MOPSO) algorithm named TOPSIS Fuzzy MOPSO (TFMOPSO) is proposed to solve the LW optimization problem. This algorithm utilizes the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) to rank the solutions, while a fuzzy inference system is developed to select the algorithm strategy for finding two leaders among the non-dominated solutions. The performance of the proposed TFMOPSO has been tested on the optimization problem of the LW of the Ute dam. The results of TFMOPSO, along with three other state-of-the-art multi-objective algorithms, are explored in terms of hypervolume, coverage, and spacing metrics. It is demonstrated that the TFMOPSO outperforms other algorithms and studies for solving the LW multi-objective optimization problem for the case of Ute dam. Also, RBFNN is found to be one of the most appropriate approaches among studied algorithms in estimating the discharge coefficient of LW, while Pareto optimal solutions from TFMOPSO exhibit a significant improvement compared to the original design of Ute dam LW.

关键词： Optimization Discharges (electric) Dams Shape Particle swarm optimization Mathematical model Genetic algorithms Labyrinth Weir swarm intelligence algorithms cost reduction shape optimization soft computing techniques

来源：评论

学校读者我要写书评

暂无评论

Improved fuzzy clustering with swarm intelligence for medical image analysis 6

Improved fuzzy clustering with swarm intelligence for medica...

引用

6th International Conference on Signal Processing and Intelligent Systems (ICSPIS)

作者： Gholami, Fateme Sadjad Univ Technol Fac Comp Engn & Informat Technol Mashhad Razavi Khorasan Iran

ISBN: (纸本)9781728186290

One of the challenges in the world today is the existence of a variety of diseases, some of which require the processing of medical images to diagnose and evaluate, such as images of brain tumors. One of the methods of analyzing and evaluating patients related to the brain is magnetic resonance imaging(MRI). Data mining methods such as clustering can be used to analyze magnetic resonance images. Clustering techniques can take the area of brain tumors from brain tissue and use it to diagnose disease. Various clustering methods have been proposed so far, one of which is the fuzzy clustering or FCM method, and it has a high accuracy for clustering and segmentation of brain tissues. Fuzzy clustering is less sensitive to the noise in these images and therefore its segmentation accuracy is somewhat desirable. To improve the performance of FCM clustering, in identifying the edges and borders of tumors, it is necessary to select the optimal clustering centers. The optimal selection of cluster centers increases its accuracy in learning and segmentation. Given that the optimal selection of cluster centers is an optimization method, metaheuristic algorithms can be used for this purpose. In this research, swarm intelligence algorithms have been used to optimally select cluster centers in FCM. The analysis of the proposed method on a set of images of brain magnetic resonance shows that the proposed algorithm has the specificity, sensitivity, and accuracy of 96.87%, 88.36%, and 91.32% in the diagnosis of brain tumors, respectively. The proposed method of hybrid methods, such as the fuzzy method, better detects brain tumors.

关键词： swarm intelligence algorithms image processing magnetic resonance brain brain tumor

来源：评论

学校读者我要写书评

暂无评论

Hierarchical structure-based joint operations algorithm for global optimization

引用

swarm AND EVOLUTIONARY COMPUTATION 2023年 79卷

作者： Sun, Gaoji Han, Rongqing Deng, Libao Li, Chunlei Yang, Guoqing Zhejiang Normal Univ Coll Econ & Management Jinhua Peoples R China Harbin Inst Technol Sch Informat Sci & Engn Weihai Peoples R China Harbin Inst Technol Sch Elect & Informat Engn Harbin Peoples R China Hebei Univ Sch Management Baoding Peoples R China

Joint operations algorithm (JOA) is a metaheuristic algorithm based on joint operations strategy in military theory, which incorporates three core operations - offensive, defensive and regroup - and has excellent performance in global optimization problems. To enhance the optimization performance of the original JOA, we re-examine the positioning of the three core operations in balancing global exploration and local exploitation, and propose a hierarchical structure-based JOA variant (abbreviated as HSJOA) by adjusting their execution mechanism. In addition, we redesign the three core operations to give full play to their synergy effect. In the new offensive operations, we simplify its specific execution strategy, but introduce an adjustment parameter to retain valuable position information. In the modified defensive operations, we integrate the influence of officer and elite comrades on soldiers to design a Gaussian distribution-based conservative defensive strategy and one Cauchy distribution-based aggressive defensive strategy. In the novel regroup operations, we replace the original random division strategy with a sorting-based division scheme. To evaluate the optimization performance of HSJOA, we conduct comparison experiments using nine excellent algorithms to deal with four real-life optimization problems and thirty test functions from IEEE CEC 2014 testbed. The comparison results show that the overall optimization performance of HSJOA is significantly better than the original JOA, the outstanding variants (TAPSO and GODE) of two well-known algorithms and four recently proposed algorithms (EO, SOA, MPA and WOA), while inferior to two winners of IEEE CEC competition (L-SHADE and EBOwithCMAR), but HSJOA clearly outperforms L-SHADE and EBOwithCMAR in terms of the runtime consumption.

关键词： Joint operations algorithm Metaheuristic algorithms swarm intelligence algorithms Evolutionary algorithms Global optimization

来源：评论

学校读者我要写书评

暂无评论

Adam-assisted quantum particle swarm optimization guided by length of potential well for numerical function optimization

引用

swarm AND EVOLUTIONARY COMPUTATION 2023年 79卷

作者： Wang, Caicheng Wang, Zili Zhang, Shuyou Tan, Jianrong Zhejiang Univ State Key Lab Fluid Power & Mechatron Syst Hangzhou 310027 Peoples R China Zhejiang Univ Engn Res Ctr Design Engn & Digital Twin Zhejiang P Hangzhou 310027 Peoples R China

Quantum-inspired particle swarm optimization (QPSO) is a powerful computation technique that introduces quantum mechanics theory into particle swarm optimization (PSO), which has been successfully applied to address complex and challenging functional optimization problems. However, QPSO still suffers from premature convergence and trapping in the local optima. In this paper, we propose a novel QPSO variant by guiding it with the length of the potential well (LPW) and assisting it with Adam (abbreviated as Adam-LGQPSO). To select the appropriate strategy according to the evolutionary states, an LPW-guided particle update strategy is proposed, by introducing a Gaussian random vector for enhancing the information sharing among particles and endowing the swarm with adaptive randomness. Meanwhile, a particle perturbation mechanism is developed to avoid getting into a search dilemma. To balance the exploration and exploitation, a diversity function is introduced and a diversity-based LPW strategy is designed, which helps the algorithm to better adapt to the complex and variable search environment. Finally, the Adam algorithm is integrated into the QPSO to assist the initialization of the population. The effectiveness of the proposed strategies is testified by a set of experiments. With the extensive experiments on the CEC 2017 test suite, we also show that Adam-LGQPSO achieves superior performance compared to several state-of-art metaheuristics.

关键词： Quantum computation Numerical optimization swarm intelligence algorithms Particle swarm Optimization Guiding information

来源：评论

学校读者我要写书评

暂无评论

Optimization of power take-off system settings and regional site selection procedure for a wave energy converter

引用

ENERGY CONVERSION AND MANAGEMENT-X 2024年 22卷

作者： Mehdipour, Hossein Amini, Erfan Naeeni, Seyed Taghi (Omid) Neshat, Mehdi Gandomi, Amir H. Univ Tehran Coll Engn Sch Civil Engn Tehran 14179 Iran Stevens Inst Technol Dept Civil Environm & Ocean Engn Hoboken NJ 07030 USA Torrens Univ Australia Ctr Artificial Intelligence Res & Optimizat Brisbane QLD 4006 Australia Univ Technol Sydney Fac Engn & Informat Technol Ultimo NSW 2007 Australia Obuda Univ Univ Res & Innovat Ctr H-1034 Budapest Hungary

Ocean wave energy stands as a crucial component in the quest for sustainable and renewable energy sources, essential in the global effort to mitigate climate change. However, a significant challenge in this field is optimizing the efficiency of Wave Energy Converters (WECs) on a regional scale, particularly Oscillating Surge Wave Energy Converters (OSWECs). This challenge stems from the complex, nonlinear interactions between ocean waves and these devices, necessitating precise tuning of Power Take-Off (PTO) system settings and optimal placement for the highest possible performance and stability. To address this challenge, our study introduces the Hill Climb - Explorative Grey Wolf Optimizer (HC-EGWO), a novel algorithm combining local search and swarmbased global optimization strategies. This hybrid approach effectively balances exploration and exploitation in the solution space, leading to more optimal PTO settings for OSWECs. Alongside this algorithmic development, we conduct a thorough feasibility analysis based on the constraints of the flap's maximum angular motion. This ensures the optimized OSWEC operates within safe and efficient limits. In a comparative analysis with the Genetic Algorithm (GA), the Particle swarm Optimization (PSO), the artificial Gorilla Troops Optimizer (GTO), and different implementations of the GWO, our results show an improvement in power output, with the HC-EGWO method achieving up to a 3.31% increase over other variations of the GWO and 45% increase compared to all the methods. The findings of this study not only demonstrate the effectiveness of the HC-EGWO method but also provide strategic insights for the deployment of OSWECs in areas like the South Caspian Sea, where unique environmental factors imply careful consideration in the site selection process.

关键词： Ocean renewable energy Oscillating surge wave energy converter Power take-off optimization Site selection Meta-heuristic swarm intelligence algorithms

来源：评论

学校读者我要写书评

暂无评论

A New Improved Multiple Behaviors Whale Optimization Algorithm 23

A New Improved Multiple Behaviors Whale Optimization Algorit...

引用

Proceedings of the 2023 7th International Conference on Natural Language Processing and Information Retrieval

作者： Yang Zhang Department of Computer Science Kanagawa University Japan

ISBN: (纸本)9798400709227

The Whale Optimization Algorithm (WOA) has demonstrated superior performance compared with other swarm intelligence algorithms, making it widely used and applied to practical problem-solving. In this paper, an enhanced WOA is proposed, referred to as the Improved Whale Optimization Algorithm (IWOA). The convergence factor of the traditional WOA is replaced with a random trigonometric function, which considers both the historical and current best positions of the whale population during the bubble-encircling search phase, thereby accelerating the global search speed of the whale population. In the random-encircling search phase, a combination of equiprobable random selection and roulette wheel selection is used to select random positions. Additionally, a random perturbation of individuals is introduced in the spiral search phase to prevent premature convergence to local optima. The effectiveness and robustness of the IWOA are evaluated using 4 benchmark function problems, and the results are compared with three other algorithms including particle swarm optimization (PSO). The experimental results demonstrate that the proposed IWOA outperforms other algorithms in terms of convergence speed and accuracy.

关键词： Benchmark Function Problems Optimization Problem swarm intelligence algorithms Whale Optimization Algorithm

来源：评论

学校读者我要写书评

暂无评论

A survey on identification of influential users in social media networks using bio inspired algorithms

引用

Procedia Computer Science 2023年 218卷 2110-2122页

作者： Sebin Jose P. Victer Paul Indian Institute of Information Technology Kottayam 686576 India

There are 4.5 billion active social media users who are continuously generating very large amounts of social big data. This data can be a vital source for prescriptive data analytics as it contains various insights about the day-to-day involvements and activities of the user. Among the large number of user profiles in a social media network, it is critical to recognize the best nodes with maximum influentiality in order to propagate messages through them so as to reach maximum number of nodes. After identifying, this seed set of nodes can be used for effective marketing through the platform. Marketing messages which are propagated via social media networks is evaluated to be the most promising form of advertisement for any product. As in the case of any other advertising strategy, the marketing team will be looking for maximum reachability of the advertisement with minimum cost. They will select the influencers in such a way that the advertisement is propagated to the maximum nodes possible, but with only limited number of initial influencers. In this study we are evaluating the suitability of bio inspired algorithms to the influential node identification problem. We are considering a sub class of bio inspired algorithms called as ”swarm intelligence” algorithms as they exhibits the collective behaviour of an organized group of bio species analogous to a group of profiles in social networks. This study has attempted to compare 4 algorithms which were already used for predicting the seed-set of influential users. The aim is to identify the most suitable one for the target problem and improve the same in future works.

关键词： social media data analytics social big data tweet analysis influential user identification viral marketing bio inspired algorithms swarm intelligence algorithms Literature review Nature-inspired computing soft computing

来源：评论

学校读者我要写书评

暂无评论

Harris Hawks Optimization: A Formal Analysis of Its Variants and Applications 13th

Harris Hawks Optimization: A Formal Analysis of Its Variants...

引用

13th International Joint Conference on Computational intelligence (IJCCI) / 13th International Conference on Evolutionary Computation Theory and Applications (ECTA)

作者： Abu Khurma, Ruba Aljarah, Ibrahim Castillo, Pedro A. Univ Jordan King Abdullah 2 Sch Informat Technol Amman Jordan Univ Granada Dept Comp Architecture & Comp Technol ETSIIT Granada Spain Univ Granada CITIC Granada Spain

ISBN: (纸本)9789897585340

The Harris Hawks Optimization (HHO) is a recent meta-heuristic algorithm developed by Hideri in 2019. HHO algorithm has been widely utilized to solve many optimization problems in different fields. The primary objective of HHO is to define a fitness function that can successfully optimize a specific problem by finding the minimum or maximum value. This survey presents a thorough study of the algorithm, including its variants such as binary, hybridization, multi-objective and modifications. It highlights the main applications such as medical, engineering, machine learning, and network applications. Finally, the conclusion summarizes the current works on HHO and suggests possible future directions.

关键词： Harris Hawcks Optimization Algorithm HHO Meta-heuristic Optimization swarm intelligence algorithms

来源：评论

学校读者我要写书评

暂无评论

Designing a sustainable supply chain network integrated with vehicle routing: A comparison of hybrid swarm intelligence metaheuristics

引用

COMPUTERS & OPERATIONS RESEARCH 2019年 110卷 220-235页

作者： Govindan, Kannan Jafarian, Ahmad Nourbakhsh, Vahid Univ Southern Denmark Ctr Sustainable Supply Chain Engn Dept Technol & Innovat Odense Denmark ATU Fac Management & Accounting Tehran Iran Univ Calif Irvine Paul Merage Sch Business Irvine CA 92697 USA

Recently, a growing concern with sustainability has become a consideration in business operations. However, there is a lack of mathematical models that quantify environmental effects and, in particular, social impacts of supply chains because of the inherently subjective nature of these aspects. To fill this gap, this paper models a distribution network in which the triple bottom lines of sustainability are captured. Different impacts of the network on the stakeholders, including company owners, workers, consumers and society, are considered as whole. In the current model, a multi-product vehicle routing problem with time windows (MPVRPTW) as an operational decision is integrated with strategic decisions related to the network design. To solve this model, three hybrid swarm intelligence techniques (particle swarm optimization (PSO), electromagnetism mechanism algorithm (EMA), and artificial bee colony (ABC)) are proposed, and each is hybridized with variable neighborhood search (VNS) are proposed. Because metaheuristic methods are sensitive to input parameters, response surface methodology (RSM) with the multi-objective decision making (MODM) approach is applied for tuning the parameters. The proposed approaches are compared with the hybrid of genetic algorithm (GA) and VNS as the benchmark algorithm. A fair comparison is conducted by employing six metrics to evaluate the quality of the Pareto frontier obtained by the algorithms on the test problems. According to the results, the predominance of EMA is enhanced by VNS local search. (C) 2018 Elsevier Ltd. All rights reserved.

关键词： Sustainable supply chain network Environmental management Corporate social responsibility Multiple-objective optimization Vehicle routing problem swarm intelligence algorithms

来源：评论

学校读者我要写书评

暂无评论

Discrete tree seed algorithm for urban land readjustment

引用

ENGINEERING APPLICATIONS OF ARTIFICIAL intelligence 2022年 112卷 104783-104783页

作者： Koc, Ismail Atay, Yilmaz Babaoglu, Ismail Konya Tech Univ Dept Software Engn TR-42075 Konya Turkey Gazi Univ Dept Comp Engn TR-06570 Ankara Turkey Konya Tech Univ Dept Comp Engn TR-42075 Konya Turkey

Land readjustment and redistribution (LR) is an important approach used to realize development plans by converting rural lands to urban land and also providing urban infrastructure. The LR problem, which is a complex challenging real-world problem, is a discrete optimization problem because its structure is similar to TSP (Traveling Salesman Problem) and scheduling problems which are combinatorial optimization problems. Since classical mathematical methods are insufficient for solving NP (Nondeterministic Polynomial) optimization problems due to time limitations, meta-heuristic optimization algorithms are commonly utilized for solving these kinds of problems. In this paper, meta-heuristic algorithms including genetic, particle swarm, differential evolution, artificial bee, and tree seed algorithms are utilized for solving LR problems. The stated meta-heuristic algorithms are used by applying spatial-based crossover and mutation operators depending upon the LR problem on each algorithm. Moreover, a synthetic dataset is used to ensure that the quality of the solution obtained is acceptable to everyone, to prove an optimal solution easily. By utilizing the suggested spatial-based crossover and mutation operators, finding the ideal solution is aimed using the synthetic dataset. In addition, five different modifications on TSA (Tree-Seed Algorithm) are performed and used to solve LR problems. All the modified versions of TSA are carried out only by changing the mechanism of seed reproduction. The novel TSA approaches are respectively named as tcTSA (tournament current), tbTSA (tournament best), pbTSA (personal-best based), t2TSA (double tournament), and elTSA (elitism based). In the experimental studies, the hybrid approach, which includes the crossover and mutation operators, is successfully applied in all of the algorithms under equal conditions for a fair comparison. According to experimental results performed using the dataset, it can be clearly stated that especiall

关键词： swarm intelligence algorithms Evolutionary algorithms Hybrid approach Spatial-based crossover and mutation operators Efficient TSA Urban land readjustment

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：