检索结果-内蒙古大学图书馆

Multiobjective artificial fish swarm algorithm for multiple sequence alignment

INFOR 2020年第1期58卷 38-59页

作者： Dabba, Ali Tari, Abdelkamel Zouache, Djaafar Abderrahmane Mira Univ Fac Sci Comp Sci Dept Bejaia Algeria Mohamed Elbachir Elibrahimi Univ Comp Sci Dept Bordj Bou Arreridj Algeria

Multiple sequence alignment (MSA) represents a basic task for many bioinformatics applications. MSA allows finding common conserved regions among various sequences of proteins or DNA. However, to find the optimal multiple sequence alignment, it is necessary to design an efficient exploration approach that could explore a huge number of possible multiple sequence alignments. As well as, it is required to use a powerful evaluation method to assess the biological relevance of these multiple sequence alignment. To address these main problems, this article presents a multiobjective artificial fish swarm algorithm (MOAFS) to solve multiple sequence alignment. MOAFS uses the behaviors of artificial fish swarm algorithm such as the cooperation, decentralization and parallelism to ensure a good trade-off between the exploration and the exploitation of the search space of MSA problem. To preserve the quality and consistency of alignment, two fitness functions have been simultaneously used by the MOAFS algorithm: (i) Weighted Sum of Pairs to determine similar regions horizontally and (ii) Similarity function to determine vertically similar regions between the sequences of an alignment. Following the exploration of space search, the Pareto-optimal set is obtained by MOAFS which performs the optimal multiple sequence alignments for both fitness functions. The performance of MOAFS algorithm has been proved by comparing our algorithm with different progressive alignment methods, and other alignment methods based on evolutionary algorithms with singleobjective and many-objective. The experiment results conducted on BAliBASE 2.0 and BAliBASE 3.0 benchmark confirm that the MOAFS algorithm provides a greater accuracy statistical significance in terms of SP or CS scores.

关键词： Multiple sequence alignment artificial fish swarm algorithm bioinspired algorithms bioinformatics optimization algorithms evolutionary algorithm molecular biology swarm intelligence

来源：评论

学校读者我要写书评

暂无评论

A pareto artificial fish swarm algorithm for solving a multi-objective electric transit network design problem

引用

TRANSPORTMETRICA A-TRANSPORT SCIENCE 2020年第3期16卷 1648-1670页

作者： Liu, Yi Feng, Xuesong Zhang, Lukai Hua, Weixing Li, Kemeng Beijing Jiaotong Univ Sch Traff & Transportat 3 Shangyuancun Beijing 100044 Peoples R China

This study presents a multi-objective optimization model for the urban electric transit network design problem by simultaneously determining the set of transit routes, the service frequency and the location of charging depots with the objectives of minimizing the costs for passengers and operators. The constraints about the bus route, charging depot, vehicle operation and charging schedule are considered to ensure the rational design and operational feasibility of the electric transit network. The solution approach is based on a Pareto artificial fish swarm algorithm (PAFSA) in combination with the crossover and mutation operators. The transit network in an urban region of Beijing is studied in a case study, revealing that the proposed optimization model solved by the PAFSA is able to provide the Pareto optimal solutions to design a relatively large-scaled transit network for the best fits between the passengers and operators.

关键词： Electric transit network design service frequency charging depot location artificial fish swarm algorithm multi-objective optimization

来源：评论

学校读者我要写书评

暂无评论

Analysis of Network Coverage Optimization Based on Feedback K-Means Clustering and artificial fish swarm algorithm

引用

IEEE ACCESS 2020年 8卷 42864-42876页

作者： Feng, Yingying Zhao, Shasha Liu, Hui Fuyang Normal Univ Coll Informat Engn Fuyang 236041 Peoples R China

There is a certain energy loss in the process of wireless sensor network information collection. Moreover, the current network protocols and network coverage methods are not sufficient to effectively reduce system energy consumption. In order to improve the operating efficiency and service life of wireless sensor networks, this study analyzes the classic LEACH protocol, summarizes the advantages and disadvantages, and proposes a targeted clustering method based on the K-means algorithm. At the same time, in order to maximize the network coverage and minimize the energy consumption on the basis of ensuring the quality of service, a wireless sensor network coverage optimization method based on an improved artificial fish swarm algorithm was proposed. In addition, a controlled experiment is designed to analyze the effectiveness and practical effects of the proposed algorithm. The experimental results show that the method proposed in this paper has certain advantages over traditional methods and can provide theoretical references for subsequent related research.

关键词： Feedback K-means clustering artificial fish swarm algorithm network coverage algorithm optimization

来源：评论

学校读者我要写书评

暂无评论

Twin support vector machine based on improved artificial fish swarm algorithm with application to flame recognition

引用

APPLIED INTELLIGENCE 2020年第8期50卷 2312-2327页

作者： Gao, Yikai Xie, Linbo Zhang, Zhengdao Fan, Qigao Jiangnan Univ Key Lab Adv Proc Control Light Ind Minist Educ Wuxi 214122 Jiangsu Peoples R China

In this paper, a twin support vector machine (TWSVM) based on improved artificial fish swarm algorithm (IAFSA) for fire flame recognition is proposed in view of the large computation burden and slow classification speed of the traditional support vector machine (SVM). Twin support vector machine is a machine learning algorithm developing from standard support vector machine. However, twin support vector machine cannot deal with the parameter selection problem well. The difficulty of parameter selection may greatly restrict the application of TWSVM in flame recognition problem. So a novel artificial fish swarm algorithm (AFSA) is used to solve the parameter selection problem of TWSVM. In order to make up for the drawbacks of the basic AFSA, the chaotic transformation is first applied to initialize the position of artificial fish swarm since it may be non-uniformly initialized in the basic artificial fish swarm algorithm. Then, the Cauchy mutation is used to make the fish swarm jump out of the local optimal solution after continuously expanding the visual scope of the artificial fish during the foraging procedure. An adaptively step-size adjusting method is then developed to optimize the moving steps of the swarming and following behaviors in order to accelerate the convergence speed of the developed algorithm. Last, to further improve the efficiency and accuracy of the algorithm, an elimination and regeneration mechanism based on adaptive t-distribution mutation is utilized to update the artificial fish swarm at each iterative procedure. Experimental results show that the TWSVM algorithm based on improved artificial fish swarm algorithm is a more effective method to identify the flame and greatly improves the accuracy and real-time performance of the flame recognition compared with PSO-TWSVM, Grid-TWSVM, GA-TWSVM, FOA-TWSVM, GSO-TWSVM, AFSA-TWSVM and the traditional SVM.

关键词： Twin support vector machine artificial fish swarm algorithm Fire flame recognition Color model

来源：评论

学校读者我要写书评

暂无评论

Optimal Power Flow Based on Novel Multi-objective artificial fish swarm algorithm

引用

ENGINEERING LETTERS 2020年第2期28卷 542-550页

作者： Guo, Gang Qian, Jie Li, Shuaiyong Luoyang Normal Univ Sch Informat Technol Luoyang 471934 Henan Peoples R China Chongqing Univ Posts & Telecommun Key Lab Complex Syst & Bion Control Chongqing 400065 Peoples R China Chongqing Univ Posts & Telecommun Key Lab Ind Internet Things & Networked Control Minist Educ Chongqing 400065 Peoples R China

Computer technology provides new possibilities for handling the many-objective optimal power flow (MOOPF) problems with high-dimension and non-differentiability. As one of typical intelligent algorithms, the novel multi-objective artificial fish swarm algorithm (NMAFSA) is proposed to solve the MOOPF problems and realize the economical operation of power systems. The NMAFSA algorithm, which combines with optimal solution guidance (OSG) principle and non-inferior retention (NIR) mechanism, is effective to reduce the fuel cost, emission and power loss. Compared with the representative many-objective particle swarm optimization (MPSO) and non-dominated sorting genetic algorithm-II (NSGA-II), the superiority and adaptability of presented NMAFSA algorithm are validated. Six simulation trials are carried out on MATLAB software, including the dual-objective and triple-objective optimizations on three different scale power systems. Detailed results demonstrate that the suggested NMAFSA algorithm with stable-operation and fast-convergence has great potential to deal with the MOOPF problems more efficiently. Furthermore, the generation distance (GD) index also quantitatively proves that the NMAFSA algorithm can obtain the well-distributed Pareto front (PF).

关键词： artificial fish swarm algorithm Optimal power flow Computer technology Generation distance

来源：评论

学校读者我要写书评

暂无评论

An artificial fish swarm algorithm for a multi-objective grain transportation problem

引用

EVOLUTIONARY INTELLIGENCE 2020年第1期13卷 9-19页

作者： Jia, Shiyu Zhou, Kang Zhou, Sisi Fang, Haocheng Zhen, Yiting Zou, Yilin Wuhan Polytech Univ Sch Math & Comp Wuhan 430023 Hubei Peoples R China

The problem of grain transportation optimization is a typical NP-complete problem. To solve the problem, it is necessary to construct a mathematical model for the optimization of grain transportation. As the single-objective grain transportation route optimization model is difficult to better simulate the complex and varied conditions in real life, the multi-objective grain transportation route optimization model is closer to reality and has more guiding significance for practical problems. Therefore, this paper constructs a multi-objective grain transportation optimization problem model. And improved the artificial fish swarm algorithm to make it can be better solution. First, a similar fragment distance is introduced to replace the traditional distance calculation method. Second, we play the guiding role of bulletin board to insert the optimal solution fragment in the bulletin board into the current solution. Finally, according to the characteristics of food transportation problems, three behaviors of artificial fish were improved and mixed neighborhood search was conducted. In simulation experiments, the precision of the traditional artificial fish algorithm and improved algorithm is more and more low with the increase of amount of data. The difference between that and the optimal solution in the database is becoming more and more big, but the error in not only path length but also the number of vehicles of the improved algorithm is still within the scope of the permit. The error of the traditional artificial fish algorithm is far beyond permissible range. Experimental results show that the improved artificial fish swarm algorithm achieves high solution accuracy in path length and the number of vehicles. However, because there is no time window constraint, the conflict between the number of vehicle and the path length is very small. Finally, the set of Pareto solutions converges to 1 or 2 points.

关键词： MOP GTOP artificial fish swarm algorithm Improved artificial fish swarm algorithm

来源：评论

学校读者我要写书评

暂无评论

Development of an artificial fish swarm algorithm Based on aWireless Sensor Networks in a Hydrodynamic Background

引用

Fluid Dynamics & Materials Processing 2020年第5期16卷 935-946页

作者： Sheng Bai Feng Bao Fengzhi Zhao Miaomiao Liu School of Computer and Information Technology Northeast Petroleum UniversityDaqing163318China

The main objective of the present study is the development of a new algorithm that can adapt to complex and changeable *** artificial fish swarm algorithm is developed which relies on a wireless sensor network(WSN)in a hydrodynamic *** nodes of this algorithm are viscous fluids and artificial fish,while related‘events’are directly connected to the food available in the related virtual *** results show that the total processing time of the data by the source node is 6.661 ms,of which the processing time of crosstalk data is 3.789 ms,accounting for 56.89%.The total processing time of the data by the relay node is 15.492 ms,of which the system scheduling and the Carrier Sense Multiple Access(CSMA)rollback time of the forwarding is 8.922 ms,accounting for 57.59%.The total time for the data processing of the receiving node is 11.835 ms,of which the processing time of crosstalk data is 3.791 ms,accounting for 32.02%;the serial data processing time is 4.542 ms,accounting for 38.36%.Crosstalk packets occupy a certain amount of system overhead in the internal communication of nodes,which is one of the causes of node-level *** show that optimizing the crosstalk phenomenon can alleviate the internal congestion of nodes to some extent.

关键词： artificial fish swarm algorithm wireless sensor network network measurement hydrodynamics

来源：评论

学校读者我要写书评

暂无评论

Research on Port Logistics Distribution Route Planning Based on artificial fish swarm algorithm

引用

JOURNAL OF COASTAL RESEARCH 2020年第Spe115期36卷 78-80页

作者： Ouyang, Fei Henan Inst Econ & Trade Zhengzhou 450046 Peoples R China

As a sub-problem of the inventory routing problem, the transportation routing problem is one of the main problems to be solved in studying how to reduce the total cost of logistics. Through the establishment of a reasonable distribution path, it plays a very important role in the cost, speed and efficiency of the whole logistics transportation. Based on the concept of artificial fish swarm algorithm, combined with the characteristics of port logistics distribution process, this paper constructs an optimization model and uses artificial fish swarm algorithm to solve specific cases. Finally, Matlab 6.5 is used to program and the artificial fish swarm algorithm is verified, which proves the feasibility and effectiveness of the algorithm. The results show that the mathematical model and artificial fish swarm algorithm have certain theoretical guidance and reference value for solving the port logistics distribution path planning problem.

关键词： artificial fish swarm algorithm port logistics distribution path planning

来源：评论

学校读者我要写书评

暂无评论

The Research of Flexible Scheduling of Workshop Based on artificial fish swarm algorithm and Knowledge Mining 11th

The Research of Flexible Scheduling of Workshop Based on Art...

引用

11th International Conference on swarm Intelligence (ICSI)

作者： Peng, Jieyang Wang, Jiahai Wang, Dongkun Kimmig, Andreas Ovtcharova, Jivka Tongji Univ Coll Mech Engn Siping Rd Shanghai 200083 Peoples R China Univ Stuttgart Keplerstr 7 D-70174 Stuttgart Germany Karlsruhe Inst Technol D-76133 Karlsruhe Germany

ISBN: (纸本)9783030539559;9783030539566

The Job Shop Scheduling problem is critical in the manufacturing industry. At present, the decision tree reasoning technique and data mining are often used in multi-objective optimization research to solve flexible job shop scheduling issues. Unfortunately, when job shop scheduling problems involve complex logic, it becomes difficult to implement data-driven automatic scheduling-without human intervention. Based on the analysis of mass data and specialized knowledge in the scheduling domain, an ontology-based scheduling knowledge model and a method of knowledge representation can be established. Considering the relationship between data mining and knowledge, this paper illustrates the acquisition process of scheduling rules. These scheduling rules were applied to improve the initialization process of the artificial fish algorithm. Then, a scheduling experiment was designed, the results of which show that the efficiency and accuracy of the algorithm has been improved. The desired uncertain information analysis, decision-making support for production planning and scheduling on the shop floor are provided and an adaptive scheduling algorithm for complex manufacturing systems is established by building a knowledge-based system.

关键词： Data-driven Ontology Knowledge mining Production scheduling artificial fish swarm algorithm

来源：评论

学校读者我要写书评

暂无评论

Optimization of Dynamic Economic Dispatch with Valve-Point Effect Using an Improved artificial fish swarm algorithm

Optimization of Dynamic Economic Dispatch with Valve-Point E...

引用

Chinese Automation Congress (CAC)

作者： Chen, Gonggui Gao, Miao Xiao, Xiong Chongqing Univ Posts & Telecommun Coll Automat Minist Educ Key Lab Ind Internet Things & Networked Control Chongqing 400065 Peoples R China

ISBN: (纸本)9781728176871

The optimization of dynamic economic dispatching with valve-point effect (DED-VPE) is a complex non-convex and nonlinear optimization problem in power systems. Responding to these challenges, an improved artificial fish swarm algorithm (IAFSA) was proposed to cope with the DED-VPE problem. The classical artificial fish swarm algorithm (AFSA) has slow convergence speed and obtains solutions of low quality because of the structure defects, the fixed visual and moving step. So, this paper proposes two strategies to overcome these defects and improve the optimization ability of the AFSA, one is self-adaptive visual and moving step, and the other is a new scheme of the preying behavior. Two test systems were used to verify the effectiveness of the proposed method. The findings of case study show the feasibility and effectiveness of the IAFSA in the optimization of DED-VPE problem.

关键词： artificial fish swarm algorithm dynamic economic dispatch prey behavior valve-point effect visual and moving step

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：