检索结果-内蒙古大学图书馆

IEEE International Conference on Computing (ICOCO)

作者： Napalit, Arcely P. Ballera, Melvin A. Technol Inst Philippines Manila Philippines

ISBN: (纸本)9781665436892

Metaheuristics offer a high-quality solution in a short period of time for real-world situations;they are a fascinating field of research that has made significant advances in solving problems. bio-inspired Swarm Intelligent (SI) algorithm is represented in different studies' umbrellas, where inspiration and principles of biological evolution develop a new way and robust techniques in computing. The paper's primary purpose is to apply the Firefly algorithm process in scheduling. The study experimented and tested the efficiency of the developed objective function based on the scheduling policy of RLMH. The experiment focused on the five different iterations and two values for attractiveness. Based on the findings that FA generates different results with different values of attractiveness and iterations. Comparing attractiveness shows a significant improvement in beta=2 in generating best solutions, but not the fireflies' movements and time execution. The experiment achieved 66.37% of the objective function.

关键词： bio-inspired algorithm exploration and exploitation fireflies scheduling

来源：评论

学校读者我要写书评

暂无评论

Sheepdog Driven algorithm for Sheep Herd Transport 40

Sheepdog Driven Algorithm for Sheep Herd Transport

引用

40th Chinese Control Conference (CCC)

作者： Liu, Yu Li, Xin Lan, Meiqi He, Yaqi Cai, He Gao, Huanli South China Univ Technol Sch Automat Sci & Engn Guangzhou 510641 Peoples R China

ISBN: (纸本)9789881563804

So far, most of the existing works on swarm systems are concerned with the collective motions. In this work, a somehow reverse problem is investigated, i.e., how an agent can interact with/control a swarm system. In particular, we consider the sheep herd transport problem: a herd of sheep is driven by a single sheepdog from the start place to the sheepfold. The rules governing the behaviors of the sheep are preset and known to the sheepdog, and the objective is to design the rule for the sheepdog to fulfil the transport task. inspired by the behaviors of true sheepdogs, we have developed a backward semi-circle reciprocation algorithm featuring dynamic turn-around point selection. Numerical results are provided to evaluate the proposed algorithm.

关键词： artificial intelligence bio-inspired algorithm sheep herd transport swarm system

来源：评论

学校读者我要写书评

暂无评论

Ecological Analogy for Generative Adversarial Networks and Diversity Control

引用

JOURNAL OF PHYSICS-COMPLEXITY 2023年第1期4卷 01LT01-01LT01页

作者： Nakazato, Kenichi Robert Bosch Japan Bosch Ctr Artificial Intelligence Tokyo Japan

Generative adversarial networks are popular deep neural networks for generative modeling in the field of artificial intelligence. In the generative modeling, we want to output a sample with some random numbers as an input. We train the artificial neural network with a training data set for the purpose. The network is known with astonishingly fruitful demonstrations, but we know the difficulty in the training because of the complex training dynamics. Here, we introduce an ecological analogy for the training dynamics. With the simple ecological model, we can understand the dynamics. Furthermore, a controller for the training can be designed based on the understanding. We then demonstrate how the network and the controller work with an ideal case, MNIST.

关键词： deep neural networks ecological dynamics generative adversarial networks training dynamics bio-inspired algorithm

来源：评论

学校读者我要写书评

暂无评论

Improving the Speed of Machine Learning algorithms using bio-inspired Techniques

Improving the Speed of Machine Learning Algorithms using Bio...

引用

IEEE International Conference on Electrical, Computer, and Energy Technologies (ICECET)

作者： Akinyelu, Andronicus A. Univ Free State Dept Comp Sci & Informat Bloemfontein Free State South Africa

ISBN: (纸本)9781665442312

Today, digital data is exploding at a breakneck speed. Traditional data analytics techniques, unfortunately, are rapidly losing their capabilities and efficiencies when dealing with large datasets. This problem has prompted several researchers to develop more effective, efficient, and fast big data analytics tools. Machine Learning (ML)-based approaches are among the most dependable techniques used to extract usable insights from large datasets. However, some of them cannot efficiently handle large datasets, and their training time grows with dataset size. This paper presents two Nature-inspired techniques for improving the training time of ML algorithms and the processing time of big datasets. The techniques are evaluated on four ML algorithms and large or medium-scale datasets. Results show that the training time of the four ML algorithms was reduced without a significant drop in classification accuracy. Moreover, the proposed methods are significantly faster than two well-known instance selection methods. Furthermore, statistical analysis reveals that the techniques reduced data size significantly, making them suitable for processing large datasets.

关键词： machine learning bio-inspired algorithm data reduction big data processing

来源：评论

学校读者我要写书评

暂无评论

Cooperative Driven algorithm for Sheep Herd Trajectory Tracking by Two Sheepdogs 40

Cooperative Driven Algorithm for Sheep Herd Trajectory Track...

引用

40th Chinese Control Conference (CCC)

作者： Dong, Qizhang Jiang, Mingjun Xu, Xinhang Liu, Yu He, Yaqi Cai, He Gao, Huanli South China Univ Technol Sch Automat Sci & Engn Guangzhou 510641 Peoples R China

ISBN: (纸本)9789881563804

In this paper, we consider the design of sheep herd driven algorithm by two sheepdogs. The sheep herd follow simple interaction rules among sheep, namely, separation and cohesion, which are treated as prior knowledge to the sheepdogs. The objective for the sheepdogs is to drive the sheep herd to follow a desired trajectory in a cooperative way. inspired by the behaviors of real sheepdogs, for each sheepdog, we have developed a semi-circle reciprocation algorithm featuring dynamic turn-around point selection taken into consideration the ingredients of the positions of the sheep, the desired trajectory and the position of the other sheepdog. Numerical simulations are conducted to examine the performance of the proposed algorithm.

关键词： bio-inspired algorithm computational intelligence sheep herd trajectory tracking swarm system

来源：评论

学校读者我要写书评

暂无评论

The capacity constraint physarum solver

引用

JOURNAL OF COMPUTATIONAL SCIENCE 2022年第0期62卷

作者： Huang, Yusheng Chu, Dong Deng, Yong Cheong, Kang Hao Univ Elect Sci & Technol China Inst Fundamental & Frontier Sci Chengdu 610054 Peoples R China Univ Elect Sci & Technol China Sch Informat Engn Chengdu 610054 Peoples R China Singapore Univ Technol & Design Sci Math & Technol Cluster S487372 Singapore Singapore Univ Elect Sci & Technol China Sch Commun Engn Chengdu 610054 Peoples R China

Physarum polycephalum inspired algorithm (PPA) has attracted immense attention recently. By modeling real-world problems into a graph and adopting proper adaption equation, PPA could solve system optimization or user equilibrium problems. However, few PPA-based algorithms can tackle problems with capacity constraints, lacking the ability to control link flows in the PPA. To tackle this problem, a novel framework, the capacitated PPA (CPPA), is proposed to allow capacity constraints toward link flow in the PPA. To demonstrate the validity of the CPPA, we have developed three applications of the CPPA, i.e., the CPPA for the maximum-flow problem (CPPA-MF), the CPPA for the minimum-cost-maximum-flow problem (CPPA-MCMF), and the CPPA for the link-capacitated traffic assignment problem (CPPA-CTAP). In the experiments, the CPPA-MF shows competitive performance among four baseline algorithms;the CPPA-MCMF is approximately 50% faster than the baseline algorithms;the CPPA-CTAP reduce the oversaturated degree from approximately 40% to 1.5% compared to the original algorithm. The experimental results prove the validation of the CPPA framework. The CPPA is also robust since it have been successfully applied in three different scenarios. The proposed CPPA extends the PPA's application to capacity-constrain problems and might shed some lights on its future applications in complex real-world problems.

关键词： Physarum polycephalum inspired algorithm bio-inspired algorithm Capacity constraints

来源：评论

学校读者我要写书评

暂无评论

Intelligent slime mold algorithm for proficient jamming attack detection in wireless sensor network

Global Transitions Proceedings

引用

Global Transitions Proceedings 2022年第2期3卷 386-391页

作者： Amirthasaravanan Arivunambi Arjun Paramarthalingam Department of Computer Science and Engineering University College of Engineering Villupuram Tamilnadu India

Wireless Sensor Networks (WSNs) depicts to the group of spatially distributed sensor nodes, each node is having individual battery for power consumption which senses the factors like Temperature, Pressure, Soil makeup, Vehicular movement, and checks the physical presence of objects, which are communicate through internet and passing messages between nearest neighbor nodes. WSN are extensively used in several applications like Environmental Monitoring, Area Monitoring, Engineering Monitoring, Health Care Monitoring, and Military Applications. Since WSN are highly susceptible for many attacks especially Jamming Attack. This causes high energy consumption, reduces the life time of nodes, affects the entire sensor networks and most significantly also the confidentiality and integrity of the data packets which are transmitted through dissimilar nodes in the network. In order to overcome these issues, a new bio-inspired algorithm called Intelligent Slime Mold (ISM) has been introduced for detection jamming node and to setting up of alternate optimal path form source node to destination node in WSN to avoid jammer affected nodes by applying Context Aware Decision Rule (CADR). This work ensures the confidentiality and integrity of both the network and data packets.

关键词： Jamming attack Wireless sensor network Intelligent slime mold algorithm Distributed sensor nodes bio-inspired algorithm

来源：评论

学校读者我要写书评

暂无评论

Hyper spectral image classifications for monitoring harvests in agriculture using fly optimization algorithm

引用

COMPUTERS & ELECTRICAL ENGINEERING 2022年 103卷

作者： Shitharth, S. Manoharan, Hariprasath Alshareef, Abdulrhman M. Yafoz, Ayman Alkhiri, Hassan Mirza, Olfat M. Kebri Dehar Univ Dept Comp Sci Kebri Dehar Ethiopia Panimalar Engn Coll Dept Elect & Commun Engn Chennai 600123 India King Abdulaziz Univ Fac Comp & Informat Technol Dept Informat Syst Jeddah Saudi Arabia Al Baha Univ Fac Comp Sci & Informat Technol Dept Comp Sci Al Baha Saudi Arabia Umm Al Qura Univ Coll Comp & Informat Syst Dept Comp Sci Mecca Saudi Arabia

Many cutting-edge technologies with regard to agricultural applications are not being employed by farmers for a number of reasons, including the fact that each piece of designed equipment is manufactured for a specific utilisation mechanism. In contrast, the use of hyperspectral remote sensing techniques is expanding to deliver more valuable data at a cheaper cost. The hyper -spectral images are created to operate in different locations using different band topologies, making the proposed model more practical and effective with the existing spectrum elements. Since flies movements are used to acquire hyperspectral images with straight-line perception, the proposed method also makes use of the bio-inspired Fly Optimization algorithm (FOA). The functional efficacy, loss prevention, and error prevention of the FOA, which averages 83 percent, show that it is far better than current practises.

关键词： Remote sensing Hyperspectral images bio-inspired algorithm Agriculture

来源：评论

学校读者我要写书评

暂无评论

An Accelerated Physarum Solver for Network Optimization

引用

IEEE TRANSACTIONS ON CYBERNETICS 2020年第2期50卷 765-776页

作者： Gao, Cai Zhang, Xiaoge Yue, Zhiying Wei, Daijun SUNY Buffalo Dept Ind & Syst Engn Buffalo NY 14228 USA Vanderbilt Univ Dept Civil & Environm Engn Sch Engn Nashville TN 37235 USA SUNY Buffalo Dept Commun Buffalo NY 14228 USA Hubei Univ Nationalitis Sch Sci Enshi 445000 Peoples R China

As a novel computational paradigm, Physarum solver has received increasing attention from the researchers in tackling a plethora of network optimization problems. However, the convergence of Physarum solver is grounded by solving a system of linear equations iteratively, which often leads to low computational performance. Two factors have been highlighted along the process: 1) high time complexity in solving the system of linear equations and 2) extensive iterations required for convergence. Thus, Physarum solver has been largely restricted by its unsatisfactory computational performance. In this paper, we aim to address these two issues by developing two enhancement strategies: 1) pruning inactive nodes and 2) terminating Physarum solver in advance. First, extensive nodes and edges become and stay inactive after a few iterations in identifying the shortest path. Removing these inactive nodes and edges significantly decreases the graph size, thereby reducing computational complexity. Second, we define a transition phase for edges. All of the paths experiencing such a transition phase are dynamically aggregated to form a set of near-optimal paths among which the optimal path is included. Depth-first search is then leveraged to identify the optimal path from the near-optimal paths set. Earlier termination of Physarum solver saves considerable iterations while guaranteeing the optimality of the found solution. Empirically, 20 randomly generated sparse and complete graphs with network sizes ranging from 50 to 2000 as well as two real-world traffic networks are used to compare the performance of accelerated Physarum solver to the other two state-of-the-art algorithms.

关键词： bio-inspired algorithm network optimization Physarum solver shortest path problem

来源：评论

学校读者我要写书评

暂无评论

Development of a multi-objective artificial tree (MOAT) algorithm and its application in acoustic metamaterials

引用

MEMETIC COMPUTING 2020年第2期12卷 165-184页

作者： Li, Qiqi He, Zhichen Li, Eric Chen, Tao Wang, Qiuyu Cheng, Aiguo Changsha Univ Sci & Technol Coll Automot & Mech Engn Changsha 410114 Hunan Peoples R China Hunan Univ State Key Lab Adv Design & Mfg Vehicle Body Changsha 410082 Peoples R China Teesside Univ Sch Sci Engn & Design Middlesbrough Cleveland England Tangsteel Co Tech Ctr Tangshan Hebei Peoples R China

Although there are many algorithms that can solve the multi-objective optimization problems (MOPs) efficiently, each algorithm has its own disadvantages. The emergence of new algorithms is beneficial to make up the deficiencies of existing algorithms. inspired by the organic matter transport process and the branch update theory of the banyan, this work proposed a new bio-inspired algorithm, named the multi-objective artificial tree (MOAT) algorithm to solve the MOPs. In MOAT, an improved crossover operator and an improved self-evolution operator are introduced to update solutions, a adaptive grid method is applied to manage the non-dominated solutions, and the strategy of variable number of branches in population is adopted to enhance the accuracy of this algorithm. Many typical test functions and seven well-known multi-objective algorithms, including MOEAD, NSGAII, MOPSO, GDE3, epsilon MOEA, IBEA and MPSO/D, are applied to study the accuracy and efficiency of MOAT. Experimental tests show that the results of MOAT are better than those of the seven algorithms, and the performance of MOAT is demonstrated. In addition, this new algorithm is also applied to solve the MOPs of two-dimensional acoustic metamaterials (AMs). The key parameters of AMs are optimized by MOAT to mitigate impact load and reduce structural mass, and the performance of these AMs is significantly improved.

关键词： Multi-objective optimization problems bio-inspired algorithm Multi-objective artificial tree algorithm Acoustic metamaterials

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：