检索结果-内蒙古大学图书馆

A hybrid quantum evolutionary algorithm with cuckoo search algorithm for QoS multicast routing problem

INTERNATIONAL JOURNAL OF COMMUNICATION NETWORKS AND DISTRIBUTED SYSTEMS 2019年第3期22卷 329-361页

作者： Meraihi, Yassine Ramdane-Cherif, Amar Mahseur, Mohammed Acheli, Dalila Univ MHamed Bougara Boumerdes Appl Automat Lab Dept Automat Ave Independence Boumerdes 35000 Algeria Univ Versailles St Quentin En Yvelines LISV Lab 10-12 Ave Europe F-78140 Velizy Villacoublay France Univ Sci & Technol Houari Boumediene Fac Elect & Informat Algiers 16025 Algeria

The QoS multicast routing problem is one of the most interesting combinatorial optimisation problems for transmission in the areas of networks and distributed systems. It is known to be an NP-hard problem, so many heuristic algorithms have been employed to solve this problem. This paper proposes two-hybrid approaches based on the hybridisation of cuckoo search (CS) algorithm with quantum evolutionary algorithm (QEA) to solve the quality of service (QoS) multicast routing problem. The main idea of the first approach, named CSQEA1, is the integration of the evolutionary equation of CS in the evolutionary operator of QEA, the rotation angle of quantum gates is adjusted by the evolution equation of CS;while the main idea of the second approach, named CSQEA2, is the replacement of the evolutionary equation of QEA by the evolutionary equation of CS, the rotation angle is used to encode the new Q-bit. The aim of these approaches is to avoid the premature convergence, ensure the diversity of the solutions and improve the effectiveness and the robustness of QEA. The simulation results show the successfulness and the efficiency of our proposed algorithms compared with other existing algorithms in the literature.

关键词： quantum evolutionary algorithm cuckoo search algorithm quality of service QoS multicast routing multicast tree

来源：评论

学校读者我要写书评

暂无评论

Adjustable rotation gate based quantum evolutionary algorithm for energy optimisation in cloud computing systems

引用

INTERNATIONAL JOURNAL OF COMPUTATIONAL SCIENCE AND ENGINEERING 2024年第4期27卷 414-433页

作者： Chauhan, Jyoti Alam, Taj Jaypee Inst Informat Technol Dept Comp Sci & Engn & Informat Technol Noida 201309 UP India

The widespread adoption of cloud computing and a rapid rise in capacity and scale of data centres results in a significant increase in electricity usage, rising data centre ownership costs, and increased carbon footprints. One of the challenging research problems of this point in time is to reduce the energy consumption in cloud data centres which leads motivation of green cloud computing. This article presents an energy efficient improved quantum evolutionary algorithm (QEA) to provide optimal solution for NP-hard task scheduling problems in cloud computing. An adjustable quantum rotation gate operator with adaptively dynamic adjustment of the rotation angle is developed according to the evolutionary generations and change in fitness values in each generation which serves as a major operation to update the population of the conventional QEA. The efficiency and accuracy of proposed QEA are greatly improved to measure the energy efficiency, makespan and resource utilisation in heterogeneous CC environment. Simulation results of the CloudSim shows that proposed QEA is superior to traditional PSO and GA to solve NP-hard task scheduling problems in green CC.

关键词： cloud computing scheduling quantum evolutionary algorithm QEA rotation gate particle swarm optimisation PSO genetic algorithm energy efficiency task scheduling problem resource allocation makespan

来源：评论

学校读者我要写书评

暂无评论

quantum evolutionary algorithm based power optimization control strategy for China initiative accelerator driven subcritical system

引用

ANNALS OF NUCLEAR ENERGY 2022年 166卷

作者： Li, Jin-Yang Guo, Sheng-Miao Gu, Long Zhang, You-Peng Xu, Hu-Shan Su, Xin-Kang Wang, Yong-Quan Chinese Acad Sci Inst Modern Phys Lanzhou 730000 Peoples R China Adv Energy Sci & Technol Guangdong Lab Huizhou 516002 Peoples R China Univ Chinese Acad Sci Sch Nucl Sci & Technol Beijing 100049 Peoples R China Lanzhou Univ Sch Nucl Sci & Technol Lanzhou 730000 Peoples R China Fudan Univ Inst Modern Phys Shanghai 200433 Peoples R China

The power optimization control strategy for China initiative Accelerator Driven subcritical System (CiADS), which focuses on the study of dynamic features and the stable operation of coupling nuclear system, is one of the most important research topics for the current engineering stage that is expected to be completed in the year 2025. However, the traditional way in handling the power control problem was carried out by specialists with transcendental knowledge and experiences, which has been believed as the high labor intensity and the brute force trial-and-error process. In this context, the quantum evolutionary algorithm (QEA) has been employed to deal with the CiADS power optimization control strategy in order to fulfill the adaptive searching procedure continuously and automatically. The manipulation of proton beam intensity has been used as the main control approach, and the physical model in describing the dynamic characteristics of power control system has been constructed in coupling the lumped parameter method and the point reactor kinetics method. In addition, the logical structure of Proportional Integral Derivative (PID) controller has been established for the power control process, and the QEA has been adopted to obtain the suitable set of parameters in the PID controller. Finally, the performance of the optimization method has been verified in the predefined control scenarios, and all the numerical results show that the control method has the applicability and feasibility in solving the CiADS power control problems. (c) 2021 Elsevier Ltd. All rights reserved.

关键词： Power control strategy quantum evolutionary algorithm Optimization method Accelerator driven system PID controller

来源：评论

学校读者我要写书评

暂无评论

Improved quantum evolutionary algorithm for combinatorial optimization problem

Improved quantum evolutionary algorithm for combinatorial op...

引用

6th International Conference on Machine Learning and Cybernetics

作者： Zhang, Rui Gao, Hui Harbin Univ Sci & Technol Sch Automat Harbin 150080 Peoples R China Harbin Inst Technol Sch Commun Sci & Engn Harbin 150090 Peoples R China

ISBN: (纸本)9781424409723

The method of calculating rotation angle of quantum rotation gate plays an important role to the performance of quantum evolutionary algorithm (QEA). This paper proposes an improved quantum evolutionary algorithm (IQEA), whose core is that a new approach of adaptive calculating rotation angle of quantum rotation gate is designed on the basis of the probability amplitude ratio of the corresponding states. Rapid convergence and good global search capability characterize the performance of IQEA. Based on a typical combinatorial optimization problem - 0/1 knapsack problems, the influence of the relative parameter to the performance of IQEA is demonstrated, and then comparing experiments have been done. The results show that IQEA is superior to the previous quantum evolutionary algorithm.

关键词： quantum evolutionary algorithm improved quantum evolutionary algorithm quantum rotation gate combinatorial optimization knapsack problem

来源：评论

学校读者我要写书评

暂无评论

Multi-Objective quantum evolutionary algorithm for Discrete Multi-Objective Combinational Problem

Multi-Objective Quantum Evolutionary Algorithm for Discrete ...

引用

15th Annual International Conference on Technologies and Applications of Artificial Intelligence (TAAI)

作者： Wei, Xin Fujimura, Shigeru Waseda Univ Grad Sch Informat Prod Syst Fukuoka Japan

ISBN: (纸本)9780769542539

Weighted linear scalar, which transfers a multi-objective problem to many single objective sub-problems, is a basic strategy in traditional multi-objective optimization. However, it is not well used in many multi-objective evolutionary algorithms because of most of them are lack of balancing between exploitation and exploration for all sub-problems. This paper proposes a novel multi-objective evolutionary algorithm called multi-objective quantum evolutionary algorithm (MOQEA). quantum evolutionary algorithm is a recent developed heuristic algorithm, based on the concept of quantum computing. The most merit of QEA is that it has little q-bit individuals are evolved to obtain an acceptable result. MOQEA decomposes a multi-objective optimization problem into a number of scalar optimization sub-problems and optimizes them simultaneously. Each sub-problem is optimized by one q-bit individual. The neighboring solutions that are defined as a set of nondominated solutions of sub-problem are generated from the corresponding q-bit individual. The experimental results have demonstrated that MOQEA outperforms or performs similarly to MOGLS and NSGA-II on discrete multi-objective problems.

关键词： quantum evolutionary algorithm multi-objective optimization pareto front evolutionary algorithm

来源：评论

学校读者我要写书评

暂无评论

An Improved quantum evolutionary algorithm for High Dimension Knapsack Problem 2

An Improved Quantum Evolutionary Algorithm for High Dimensio...

引用

2nd International Conference on Information Technology and Intelligent Transportation Systems (ITITS)

作者： Hao, Li Lei, Ma Na, Qin Southwest Jiaotong Univ Sch Elect Engn Chengdu Sichuan Peoples R China

ISBN: (纸本)9781614997856;9781614997849

Knapsack problem is a typical combinatorial optimization problem, which has attracted wide attention because of its simple description, difficult solution and wide application background. For the knapsack problem, we proposed an improved quantum evolutionary algorithm. In this algorithm, the qubits are initialized according to the value densities of their corresponding items, the angle of Q-gate is determinated through direct comparison between the quantum chromosome and the optimal solution, random crossover method is taken to share information among chromosomes, and HE gate is used to prevent from premature convergence. Finally, experiment demonstrates the effectiveness of the algorithm.

关键词： knapsack problem quantum evolutionary algorithm qubit Q-gate HE gate

来源：评论

学校读者我要写书评

暂无评论

QoS Multicast Routing based on a Hybrid quantum evolutionary algorithm with Firefly algorithm 5

QoS Multicast Routing based on a Hybrid Quantum Evolutionary...

引用

5th International Conference on Electrical Engineering - Boumerdes (ICEE-B)

作者： Mahseur, Mohammed Meraihi, Yassine Boukra, Abdelmadjid Ramdane-Cherif, Amar Univ Sci & Technol Houari Boumediene Fac Elect & Informat Algiers 16025 Algeria Univ MHamed Bougara Boumerdes Appl Automat Lab Ave Independence Boumerdes 35000 Algeria Univ Versailles St Quentin en Yvelines LISV Lab 10-12 Ave Europe F-78140 Velizy Villacoublay France

ISBN: (纸本)9781538606865

The quality of service multicast routing problem (QoSMRP) is a very interesting research issue for transmission in communication networks. It is known to be an NP-hard problem, so many heuristic algorithms have been employed for solving this problem. This paper proposes two hybrid approaches based on the hybridization of Firefly algorithm (FA) with quantum evolutionary algorithm (QEA) to solve the QoS multicast routing problem. The main idea of the first approach, named FAQEA1, is the integration of the evolutionary equation of FA in the evolutionary operator of QEA;while the main idea of the second approach, named FAQEA2, is the replacement of the evolutionary equation of QEA by the evolutionary equation of FA. The aim of these approaches is to avoid the premature convergence, ensure the diversity of the solutions and enhance the efficiency of QEA. The experimental results reveal the superiority and effectiveness of our proposed algorithms compared with other existing algorithms.

关键词： quantum evolutionary algorithm Firefly algorithm Quality of Service (QoS) Multicast routing Multicast tree

来源：评论

学校读者我要写书评

暂无评论

Imploved Real quantum evolutionary algorithm and Application

Imploved Real Quantum Evolutionary Algorithm and Application

引用

International Conference on Measurement, Instrumentation and Automation (ICMIA 2012)

作者： Chen, Jiancheng Tu, Angyan Zhejiang Ind Polytech Coll Sch Comp Shaoxing 312000 Zhejiang Peoples R China Shaoxing Univ Dept Comp Shaoxing 312000 Zhejiang Peoples R China

ISBN: (纸本)9783037855461

This paper proposes an improved evolutionary computing method called an improved real quantum evolutionary algorithm (IRQEA) based on analysing the traditional quantum evolutionary algorithm. quantum chromosomes are divided into real components andprob ability amplitude components in IRQEA. One real component shows one candidate real solution obtained the optimal solution by gauss variation. Probability amplitude components maintain the population diversity through trigonometric function changes and realize more candidate solutions by less chromosomes, so as to achieve the algorithm's better optimization abilities. Through the simulation experiments of seven common functions obtaining the optima 1 values,it shows that IRQEA have many good characteristics,such as fast convergence rate and best performance

关键词： quantum evolutionary algorithm Function Optimization Real quantum evolutionary algorithm High Frequency Mutation

来源：评论

学校读者我要写书评

暂无评论

A Local Search Operator in quantum evolutionary algorithm and its Application in Fractal Image Compression

A Local Search Operator in Quantum Evolutionary Algorithm an...

引用

2nd International Conference on Computer and Automation Engineering (ICCAE)

作者： Mohamadi, Hossein Nodehi, Ali Tayarani, Mohamad Islamic Azad Univ Azadshahr Branch Tehran Iran Islamic Azad Univ Gorgan Branch Tehran Iran Islamic Azad Univ Mashhad Branch Tehran Iran

ISBN: (纸本)9781424455690

Fractal Image Compression is an optimization problem in the class of NP-Hard problems. quantum evolutionary algorithm is a novel optimization algorithm proposed for class of combinatorial problems like Knapsack problem. While QEA is highly suitable for NP-Hard problems, QEA is not widely used in Fractal Image Compression. In order to improve the performance of QEA in Fractal Image Compression, this paper proposes a local search operator for QEA. The proposed algorithm uses Simulated Annealing algorithm in its search process. The SA is performed on observed possible solution to help the algorithm escaping from local optima. The proposed Simulated Annealing quantum evolutionary algorithm (SAQEA) for fractal image compression is tested on several images like Lena, Pepper and Baboon for several times and is compared with QEA and GA. Experimental results show better performance for the proposed algorithm than QEA and GA and in comparison with full search, the proposed algorithm reaches suitable solutions with much less computation complexity.

关键词： Fractal Image Compression quantum evolutionary algorithm Simulated Annealing

来源：评论

学校读者我要写书评

暂无评论

Parallel Improved quantum evolutionary algorithm for Complex Optimization Problems 6th

Parallel Improved Quantum Evolutionary Algorithm for Complex...

引用

6th International Conference on Smart Computing and Communication (SmartCom)

作者： Sun, Yapeng Cent South Univ Sch Automat Changsha 410083 Hunan Peoples R China Hunan Univ Sci & Technol Sch Comp Sci & Engineer Xiangtan 411201 Hunan Peoples R China

ISBN: (纸本)9783030977740;9783030977733

As for the problems of premature convergence, slow convergence and long computing time in solving complex continuous function optimization by traditional quantum evolutionary algorithm, a dynamic parallel quantum evolutionary algorithm for solving complex continuous function optimization problem is proposed in this paper. Multi population co-evolution is adopted, and each sub-population evolves in different search areas according to their own evolution objectives to form a parallel search mode, which can speed up the algorithm convergence and avoid premature convergence;quantum computation is introduced into the differential evolution algorithm. In this method, the probability amplitude representation of qubits is applied to the real number coding of chromosomes, the chromosome position is updated by quantum mutation, quantum crossover and quantum selecting operations, the two probability amplitudes of qubits are exchanged by quantum non-gate, and an adaptive operator is introduced to improve the population diversity, It can not only prevent the premature convergence of the algorithm, but also make the algorithm converge faster and improve the problem-solving ability of the optimization algorithm. Taking the function extreme value problem as an example, the effectiveness of the algorithm is verified by this algorithm.

关键词： Parallel computing Message passing interface quantum evolutionary 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：