检索结果-内蒙古大学图书馆

immune optimization algorithm for solving joint call admission control problem in next-generation wireless network

ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2012年第7期25卷 1395-1402页

作者： Zhu, Si-feng Liu, Fang Qi, Yu-tao Chai, Zheng-yi Wu, Jian-she Xidian Univ Sch Comp Sci & Technol Xian 710071 Peoples R China Zhoukou Normal Univ Dept Math & Informat Sci Zhoukou 466001 Peoples R China Xidian Univ Key Lab Intelligent Percept & Image Understanding Minist Educ China Xian 710071 Peoples R China

The integration of radio access networks with different radio access technologies (RATs) is one of the remarkable characteristics of the next-generation wireless networks (NGWNs). In NGWN, the users with multi-network interface terminals should be able to select independently radio access network to obtain the best service. Therefore, joint call admission control (JCAC) schemes are required to select the most appropriate radio access network (RAN) for incoming calls. We propose an immune algorithm-based JCAC (IA-JCAC) scheme with users centric in order to enhance user's satisfaction. However, JCAC algorithms with users centric can lead to highly unbalanced traffic load among the available RANs in NGWN because users act independently, and most of them may prefer to be connected through a particular RAN. Highly unbalanced traffic load in NGWN will result in high overall call blocking/dropping probability and poor radio result utilization. To solve this problem, we employ dynamic pricing for balancing traffic load among available RANs in heterogeneous wireless networks where users' preferences are considered in decision-making on RAT selection. The proposed IA-based JCAC scheme is compared with another scheme that does not use the dynamic pricing on the performance. The simulation result shows the effectiveness of the proposed IA-JCAC scheme is improved significantly. Crown Copyright (C) 2012 Published by Elsevier Ltd. All rights reserved.

关键词： Joint call admission control Next-generation wireless network Dynamic pricing Load balance User's preference immune optimization algorithm

来源：评论

学校读者我要写书评

暂无评论

A novel multi-population coevolution strategy for single objective immune optimization algorithm

引用

NEURAL COMPUTING & APPLICATIONS 2018年第4期29卷 1115-1128页

作者： Xiao, Jinke Li, Weimin Liu, Bin Ni, Peng Air Force Engn Univ Air & Missile Def Coll Changle East Rd Jiazi 1 Xian 710051 Shaanxi Peoples R China

A novel multi-population coevolution strategy for single objective immune optimization algorithm (MCIA) is proposed to solve numerical and engineering optimization problem in real world from the inspiration that how neuro-endocrine system affects T cells and B cells in immune system eliminate the danger. The main idea of MCIA is to promote three populations to coevolution through self-adjusted clone operator, the applied dislocation arithmetic crossover, cloud self-adapting mutation operator and local search operator to produce lymphocyte with high affinity, where several operators have the capability of broadening the elites search space, boosting the global and local search around elites in search space. The MCIA is population B, population T, and assistant population A carrying on parallel evolution in nature, which simulates the immune system more comprehensively and unique in the aspects: clone operator and selected elite elements in the memory population enable the search space be broadened and compressed, and with the help of the cloud model characterized with randomness and stable topotaxis and local search technique, the global and local search is integrated to find the global optima with high population diversity. The performance comparisons of MCIA with three known immune algorithms and three optimization algorithms in optimizing 12 benchmark functions indicate that MCIA is an effective algorithm for solving global optimization problems with high precision, good robustness and low time complexity.

关键词： immune optimization algorithm Neuro-endocrine system Coevolution strategy

来源：评论

学校读者我要写书评

暂无评论

A novel multi-population coevolution immune optimization algorithm

引用

SOFT COMPUTING 2016年第9期20卷 3657-3671页

作者： Xiao, Jinke Li, Weimin Liu, Bin Ni, Peng Air Force Engn Univ Air & Missile Def Coll Changle East Rd Jiazi 1 Xian 710051 Peoples R China

A novel multi-population coevolution immune optimization algorithm (MCIA) is proposed to solve numerical and engineering optimization problem in real world. MCIA is inspired by the mechanism that how neuroendocrine system affects T cells and B cells in immune system to eliminate the danger and the main idea of MCIA is to promote three populations, population B, population T and assistant population A, to coevolution through self-adjusted clone operator, the applied dislocation arithmetic crossover, cloud self-adapting mutation operator and local search operator to produce lymphocyte with high affinity. Self-adjusted clone operator and selecting elite elements in the memory population enable the search space be broadened and compressed, cloud self-adapting mutation operator characterized with randomness, stable topotaxis and local search technique enable global and local search be integrated to find the global optima with high population diversity. Therefore, several operators enable MCIA enjoy the capability of broadening the elite search space, boosting the global and local search around elites in search space. The performance comparisons of MCIA with three known immune algorithms and other three optimization algorithms in optimizing twelve benchmark functions indicate that MCIA is an effective algorithm for solving global optimization problems with high precision, good robustness and low time complexity.

关键词： immune optimization algorithm Neuroendocrine system Cloud model Coevolution

来源：评论

学校读者我要写书评

暂无评论

optimization of Bimetallic Cu-Au and Ag-Au Clusters by Using a Modified Adaptive immune optimization algorithm

引用

JOURNAL OF COMPUTATIONAL CHEMISTRY 2009年第13期30卷 1992-2000页

作者： Wu, Xia Cai, Wensheng Shao, Xueguang Nankai Univ Coll Chem Res Ctr Analyt Sci Tianjin 300071 Peoples R China

A modified adaptive immune optimization algorithm (AIOA) is designed for optimization of Cu-Au and Ag-Au bimetallic clusters with Gupta potential. Compared with homoatom clusters, there are homotopic isomers in bimetallic cluster, so atom exchange operation is presented in the modified AIOA. The efficiency of the algorithm is tested by optimization of CunAu38-n (0 <= n <= 38). Results show that all the structures with the putative global minimal energies are successfully located. In the optimization of AgnAu55-n (0 <= n <= 55) bimetallic clusters, all the structures with the reported minimal energies are obtained, and 36 structures with even lower potential energies are found. On the other hand, with the optimized structures of CunAu55-n it is shown that all 55-atom Cu-Au bimetallic clusters are Mackay icosahedra except for Au-55, which is a face-centered cubic (fcc)-like structure;Cu-55, Cu12Au43, and Cu1Au54 have two-shell Mackay icosahedral geometries with I-h point P,roup symmetry. (C) 2009 Wiley Periodicals, Inc. J Comput Chem 30: 1992-2000, 2009

关键词： global optimization immune optimization algorithm bimetallic cluster Gupta potential

来源：评论

学校读者我要写书评

暂无评论

A parallel immune optimization algorithm for numeric function optimization

引用

EVOLUTIONARY INTELLIGENCE 2008年第3期1卷 171-185页

作者： Lau, Henry Y. K. Tsang, Wilburn W. P. Univ Hong Kong Dept Ind & Mfg Syst Engn Pokfulam Rd Hong Kong Hong Kong Peoples R China

immune optimization algorithms show good performance in obtaining optimal solutions especially in dealing with numeric optimization problems where such solutions are often difficult to determine by traditional techniques. This article presents the parallel suppression control algorithm (PSCA), a parallel algorithm for optimization based on artificial immune systems (AIS). PSCA is implemented in a parallel platform where the corresponding population of antibodies is partitioned into subpopulations that are distributed among the processes. Each process executes the immunity-based algorithm for optimizing its subpopulation. In the process of evolving the solutions, the activities of antibodies and the activities of the computation agents are regulated by the general suppression control framework (GSCF) which maintains and controls the interactions between the populations and processes. The proposed algorithm is evaluated with benchmark problems, and its performance is measured and compared with other conventional optimization approaches.

关键词： Artificial immune systems immune optimization algorithm Function optimization Parallel implementation

来源：评论

学校读者我要写书评

暂无评论

An adaptive immune optimization algorithm with dynamic lattice searching operation for fast optimization of atomic clusters

引用

CHEMICAL PHYSICS 2014年 440卷 94-98页

作者： Wu, Xia Wu, Genhua Anqing Normal Univ Sch Chem & Chem Engn Anqing 246011 Peoples R China

Geometrical optimization of atomic clusters is performed by a development of adaptive immune optimization algorithm (AIOA) with dynamic lattice searching (DLS) operation (AIOA-DLS method). By a cycle of construction and searching of the dynamic lattice (DL), DLS algorithm rapidly makes the clusters more regular and greatly reduces the potential energy. DLS can thus be used as an operation acting on the new individuals after mutation operation in AIOA to improve the performance of the AIOA. The AIOA-DLS method combines the merit of evolutionary algorithm and idea of dynamic lattice. The performance of the proposed method is investigated in the optimization of Lennard-Jones clusters within 250 atoms and silver clusters described by many-body Gupta potential within 150 atoms. Results reported in the literature are reproduced, and the motif of Ag-61 cluster is found to be stacking-fault face-centered cubic, whose energy is lower than that of previously obtained icosahedron. (C) 2014 Elsevier B.V. All rights reserved.

关键词： Global optimization immune optimization algorithm Dynamic lattice searching Lennard-Jones clusters Silver clusters

来源：评论

学校读者我要写书评

暂无评论

A modified immune optimization algorithm

A modified immune optimization algorithm

引用

5th International Conference on Machine Learning and Cybernetics

作者： Song, Zhuo-Yue Gao, X. Z. Huang, Xian-Lin Lin, H. S. Harbin Inst Technol Ctr Control Theory & Guidance Technol Harbin 150006 Peoples R China Aalto Univ Inst Intelligent Power Elect Espoo Finland

ISBN: (纸本)1424400600

This paper proposes a modified immune optimization algorithm for the multi-modal problems. It is inspired by the clonal selection and antibody diversity maintaining principles in the immunology. Compared with the existing immune optimization algorithms, our new algorithm defines the concentration of the antibody to stand for the solution diversity, and the mutation rate is dynamically adjusted based on the antibody concentration and fitness. Moreover, this algorithm takes advantage of the clusters of antibodies. For each cluster, the elitist ones are always retained as the memory set. Therefore, an improved trade-off between the exploration and exploitation in the solution space can be achieved. A few multi-modal and high-dimension benchmark functions are utilized here to examine the efficiency off our optimization method. Performance comparisons are also made with the CLONALG and opt-aiNET. Simulation results demonstrate that this modified optimization algorithm can effectively obtain the optimal solutions, and still maintain the solution diversity, which is crucial for dealing with challenging real-world optimization problems.

关键词： immune optimization algorithm diversity clonal selection multi-modal optimization mutation rate

来源：评论

学校读者我要写书评

暂无评论

A Hybrid Dynamic Multi-objective immune optimization algorithm Using Prediction Strategy and Improved Differential Evolution Crossover Operator

A Hybrid Dynamic Multi-objective Immune Optimization Algorit...

引用

18th International Conference on Neural Information Processing (ICONIP)

作者： Ma, Yajuan Liu, Ruochen Shang, Ronghua Xidian Univ Minist Educ China Key Lab Intelligent Percept & Image Understanding Xian 710071 Peoples R China

ISBN: (纸本)9783642249570;9783642249587

In this paper, a hybrid dynamic multi-objective immune optimization algorithm is proposed. In the algorithm, when a change in the objective space is detected, aiming to improve the ability of responding to the environment change, a forecasting model, which is established by the non-dominated antibodies in previous optimum locations, is used to generate the initial antibodies population. Moreover, in order to speed up convergence, an improved differential evolution crossover with two selection strategies is proposed. Experimental results indicate that the proposed algorithm is promising for dynamic multi-objective optimization problems.

关键词： Prediction Strategy differential evolution dynamic multi-objective immune optimization algorithm

来源：评论

学校读者我要写书评

暂无评论

Application Research on Location Selection of Logistics Distribution Center Based on immune optimization algorithm

Application Research on Location Selection of Logistics Dist...

引用

2017 5th International Conference on Frontiers of Manufacturing Science and Measuring Technology(FMSMT 2017)

作者： SONG Qiang School of Mechanical Engineering Anyang Institute of Technology

ISBN: (纸本)9781510840232

With logistics cost as objective function, the immune optimization algorithm was proposed to optimize the location selection of logistics distribution center. Feasibility analysis was conducted on the logistics demand points of 31 cities in China. Results indicated that the programming of the optimization algorithm based on Matlab was simple, with quick computing speed. It can solve the location problems with time restrictions as well as the traditional location problems without time restrictions. Therefore, such new algorithm is more effective than the gravity approach to solve the location problem of logistics center.

关键词： Logistics distribution center immune optimization algorithm Location selection

来源：评论

学校读者我要写书评

暂无评论

Based on immune optimization algorithm and Hybrid Particle Swarm optimization algorithm Research on Customized Bus Station Setting and Route Planning—A Case Study of Customized Public Transport in Wuhan, China

Based on Immune Optimization Algorithm and Hybrid Particle S...

引用

2016 International Conference on Mathematical, Computational and Statistical Sciences and Engineering（MCSSE 2016）

作者： Zi-jian TAN Jing-jing ZHOU Su-hua ZHANG Dong-fang ZHAO School of Mathematics and Statistics Central China Normal University

Customized bus is a kind of new type of public transport service which is tailor-made for the people whose travel has a similar location, time, destination and so on. In 2013, it has appeared in China and landed in various cities successively. But the development around the situation is different, the study about this problem is few. Based on the immune optimization algorithm and particle swarm optimization algorithm, combined with the advantages of genetic algorithm, the paper forms a hybrid immune optimization algorithm and hybrid particle swarm algorithm. Considering the custom bus station settings, line planning and other aspects, we take custom bus in Wuhan City, Hubei Province as an example, and try to give a scientific and practical guidance to the development of customized public transport nowadays, so that it can get a further theoretical and practical basis on mathematics in its development and operation.

关键词： Customized bus Genetic algorithm immune optimization algorithm Hybrid particle swarm optimization

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：