检索结果-内蒙古大学图书馆

Computational Models & Methods in Systems Biology & Medicine

引用

IET SYSTEMS BIOLOGY 2015年第6期9卷 217-217页

作者： Ruskin, Heather J. Roznovat, Irina A.

来源：评论

学校读者我要写书评

暂无评论

Transformation operators based grey wolf optimizer for travelling salesman problem

引用

JOURNAL OF COMPUTATIONAL SCIENCE 2021年 55卷

作者： Panwar, Karuna Deep, Kusum Indian Inst Technol Roorkee Dept Math Roorkee Uttar Pradesh India

In the field of swarm intelligence, the Grey Wolf Optimizer (GWO) is a popular algorithm based on leadership hierarchy. Primarily, GWO was proposed to solve continuous optimization problem. However, in recent years, GWO has been extensively explored to deal with a wide variety of real world problem regardless the nature of problem. GWO has received a lot of attention from researchers because of its advantages over other swarm intelligence approaches and its simplicity. The classical GWO is redesigned in this paper by incorporating the swap, shift and symmetry transformation operators to solve permutation-coded travelling salesman problem (TSP), and it is named as transformation operator based grey wolf optimizer (TO-GWO). In TO-GWO, each wolf represents a possible solution of TSP and using swap, shift and symmetry operators wolves interact with leader wolves in order to obtain optimal solution for TSP. In order to improve the proposed algorithm's local search capability when solving discrete problems, 2-opt algorithm have also been adapted. The TO-GWO is implemented in MATLAB environment. In this study, the TO-GWO is tested over 50 TSP instances. Also, the results of proposed algorithm are compared with 12 state-of-the-art algorithms for TSP instances with various numbers of cities in order to evaluate its performance. For the majority of the TSP instances used in the experiment, the TO-GWO significantly outperforms other algorithms in terms of quality of solutions and efficiency.

关键词： Travelling salesman problem swarm intelligence algorithms Grey wolf optimizer 2-opt Transformation operators

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

The Digital Twin Realization of an Ejector for Multiphase Flows

引用

ENERGIES 2021年第17期14卷 5533页

作者： Mazzuto, Giovanni Ciarapica, Filippo Emanuele Ortenzi, Marco Bevilacqua, Maurizio Univ Politecn Marche Fac Engn Dipartimento Ingn Ind & Sci Matemat I-60131 Ancona Italy

Despite the extensive use of ejectors in the process industry, it is complex to predict suction and motive fluids mixture characteristics, especially with multiphase flows, even if, in most cases, mixture pressure control is necessary to satisfy process requirements or to avoid performance problems. The realization of an ejector model can allow the operators to overcome these difficulties to have real-time control of the system performance. In this context, this work proposes a framework for developing a Digital Twin of an ejector installed in an experimental plant able to predict the future state of an item and the impact of negative scenarios and faults diagnosis. ANNs have been identified as the most used tool for simulating the multiphase flow ejector. Nevertheless, the complexity in defining their structure and the computational effort to train and use them are not suitable for realizing standalone applications onboard the ejector. The proposed paper shows how swarm intelligence algorithms require a low computational complexity and overperform prediction error and computational effort. Specifically, the Grey Wolf optimizer proves to be the best one among those analyzed.

关键词： ejector multiphase flow swarm intelligence algorithms

来源：评论

学校读者我要写书评

暂无评论

CCSA: Conscious Neighborhood-based Crow Search Algorithm for Solving Global Optimization Problems

引用

APPLIED SOFT COMPUTING 2019年 85卷 105583-000页

作者： Zamani, Hoda Nadimi-Shahraki, Mohammad H. Gandomi, Amir H. Islamic Azad Univ Fac Comp Engn Najafabad Branch Najafabad Iran Islamic Azad Univ Big Data Res Ctr Najafabad Branch Najafabad Iran Univ Technol Sydney Fac Engn & Informat Technol Ultimo NSW 2007 Australia Stevens Inst Technol Sch Business Hoboken NJ 07030 USA

In this paper, a conscious neighborhood-based crow search algorithm (CCSA) is proposed for solving global optimization and engineering design problems. It is a successful improvement to tackle the imbalance search strategy and premature convergence problems of the crow search algorithm. CCSA introduces three new search strategies called neighborhood-based local search (NLS), non-neighborhood based global search (NGS) and wandering around based search (WAS) in order to improve the movement of crows in different search spaces. Moreover, a neighborhood concept is defined to select the movement strategy between NLS and NGS consciously, which enhances the balance between local and global search. The proposed CCSA is evaluated on several benchmark functions and four applied problems of engineering design. In all experiments, CCSA is compared by other state-of-the-art swarm intelligence algorithms: CSA, BA, CLPSO, GWO, EEGWO, WOA, KH, ABC, GABC, and Best-so-far ABC. The experimental and statistical results show that CCSA is very competitive especially for large-scale optimization problems, and it is significantly superior to the compared algorithms. Furthermore, the proposed algorithm also finds the best optimal solution for the applied problems of engineering design. (C) 2019 Elsevier B.V. All rights reserved.

关键词： Optimization Metaheuristic algorithms swarm intelligence algorithms Engineering problems Conscious neighborhood

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

Design on a wireless mechanomyography acquisition equipment and feature selection for lower limb motion recognition

引用

BIOMEDICAL SIGNAL PROCESSING AND CONTROL 2022年第0期77卷 000-000页

作者： Zhang, Hanyang Wang, Xinping Zhang, Yue Cao, Gangsheng Xia, Chunming East China Univ Sci & Technol Dept Mech Engn Shanghai 200237 Peoples R China Shanghai Univ Engn Sci Sch Mech & Automot Engn Shanghai 201620 Peoples R China

Mechanomyography (MMG) is a kind of biomedical signal with great research value. This paper designed a novel wireless MMG signal acquisition system composed of modular nodes and core board. The modular nodes were worn on different muscle of the lower limbs, collecting movement data of the corresponding part, and transmitted it to the core board wirelessly. The core board was connected to the computer through the USB to achieve the wireless collection and real-time display of MMG signals. In order to improve the real-time performance, this paper adopted four swarm intelligence algorithms (GA, PSO, WOA, SSA) and three improved algorithms (IPSO, IWOA, ISSA) for feature selection to reduce the redundancy of information. In this study, the different effects of different feature selection methods on the recognition of eight types of lower limb movements based on MMG signals were discussed by comparing the classification accuracy, number of iterations and calculation time. The results show that swarm intelligence algorithms have merits in this type of feature selection problem, and GA has the most obvious effect in improving classification accuracy, but it requires more iterations and calculation time, while the classification accuracy of IPSO is close to that of GA, and it has advantages in time consumption.

关键词： Mechanomyography Wireless acquisition Feature selection swarm intelligence algorithms Pattern recognition

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

A hybrid artificial bee colony algorithm for numerical function optimization

引用

INTERNATIONAL JOURNAL OF MODERN PHYSICS C 2015年第10期26卷 1550109-1550109页

作者： Alqattan, Zakaria N. Abdullah, Rosni Univ Sains Malaysia Sch Comp Sci George Town 11800 Malaysia

Artificial Bee Colony (ABC) algorithm is one of the swarm intelligence algorithms;it has been introduced by Karaboga in 2005. It is a meta-heuristic optimization search algorithm inspired from the intelligent foraging behavior of the honey bees in nature. Its unique search process made it as one of the most competitive algorithm with some other search algorithms in the area of optimization, such as Genetic algorithm (GA) and Particle swarm Optimization (PSO). However, the ABC performance of the local search process and the bee movement or the solution improvement equation still has some weaknesses. The ABC is good in avoiding trapping at the local optimum but it spends its time searching around unpromising random selected solutions. Inspired by the PSO, we propose a Hybrid Particle-movement ABC algorithm called HPABC, which adapts the particle movement process to improve the exploration of the original ABC algorithm. Numerical benchmark functions were used in order to experimentally test the HPABC algorithm. The results illustrate that the HPABC algorithm can outperform the ABC algorithm in most of the experiments (75% better in accuracy and over 3 times faster).

关键词： swarm intelligence algorithms artificial bee colony algorithm particle swarm optimization numerical function optimization

来源：评论

学校读者我要写书评

暂无评论

DMFO-CD: A Discrete Moth-Flame Optimization Algorithm for Community Detection

引用

algorithms 2021年第11期14卷 314页

作者： Nadimi-Shahraki, Mohammad H. Moeini, Ebrahim Taghian, Shokooh Mirjalili, Seyedali Islamic Azad Univ Najafabad Branch Fac Comp Engn Najafabad *** Iran Islamic Azad Univ Najafabad Branch Big Data Res Ctr Najafabad *** Iran Torrens Univ Australia Ctr Artificial Intelligence Res & Optimisat Fortitude Valley Qld 4006 Australia Yonsei Univ Yonsei Frontier Lab Seoul 03722 South Korea

In this paper, a discrete moth-flame optimization algorithm for community detection (DMFO-CD) is proposed. The representation of solution vectors, initialization, and movement strategy of the continuous moth-flame optimization are purposely adapted in DMFO-CD such that it can solve the discrete community detection. In this adaptation, locus-based adjacency representation is used to represent the position of moths and flames, and the initialization process is performed by considering the community structure and the relation between nodes without the need of any knowledge about the number of communities. Solution vectors are updated by the adapted movement strategy using a single-point crossover to distance imitating, a two-point crossover to calculate the movement, and a single-point neighbor-based mutation that can enhance the exploration and balance exploration and exploitation. The fitness function is also defined based on modularity. The performance of DMFO-CD was evaluated on eleven real-world networks, and the obtained results were compared with five well-known algorithms in community detection, including GA-Net, DPSO-PDM, GACD, EGACD, and DECS in terms of modularity, NMI, and the number of detected communities. Additionally, the obtained results were statistically analyzed by the Wilcoxon signed-rank and Friedman tests. In the comparison with other comparative algorithms, the results show that the proposed DMFO-CD is competitive to detect the correct number of communities with high modularity.

关键词： community detection complex network optimization metaheuristic algorithms swarm intelligence algorithms moth-flame optimization algorithm

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：