As a well-known combinatorial optimization problem, knapsack problems commonly arise in security areas. In this paper, an improved quantum-inspired evolutionary algorithm (PEQIEA) is proposed to solve knapsack problem...
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ISBN:
(纸本)9783319685427;9783319685410
As a well-known combinatorial optimization problem, knapsack problems commonly arise in security areas. In this paper, an improved quantum-inspired evolutionary algorithm (PEQIEA) is proposed to solve knapsack problems. In PEQIEA, in each iteration, the state preference of the elite group is used to update the group. The elite group of each iteration consists of a certain number of individuals which are selected by their fitness values. A state preference is proposed to improve the efficiency of the algorithm. A new quantum-inspired gate is obtained by the elite group and their state preference. The Q-gate is then used to make the evolution of the group. The parameters in PEQIEA, which affect the accuracy and efficiency of the algorithm, are discussed empirically. The performance of PEQIEA is then evaluated through extensive experiments.
Hardware-Software (HW-SW) co-synthesis is one of the key steps in modern embedded system design. Generally, HW-SW co-synthesis is to optimally allocate processors, assign tasks to processors, and schedule the processi...
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Hardware-Software (HW-SW) co-synthesis is one of the key steps in modern embedded system design. Generally, HW-SW co-synthesis is to optimally allocate processors, assign tasks to processors, and schedule the processing of tasks to achieve a good balance among performance, cost, power consumption, etc. Hence, it is a typical multi-objective optimization problem. In this paper, a new multi-objective HW-SW co-synthesis algorithm based on the quantum-inspired evolutionary algorithm (MQEAC) is proposed. MQEAC utilizes multiple quantum probability amplitude vectors to model the promising areas of solution space. Meanwhile, this paper presents a new crossover operator to accelerate the convergence to the Pareto front and introduces a PE slot-filling strategy to improve the efficiency of scheduling. Experimental results show that the proposed algorithm can solve the typical multi-objective co-synthesis problems effectively and efficiently.
Based on quantum-inspired evolutionary algorithm (QEA), a novel approach of constructing multi-class least squares wavelet SVM (LS-WSVM) classifiers is presented, regularization parameters and kernel parameters of LS-...
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Based on quantum-inspired evolutionary algorithm (QEA), a novel approach of constructing multi-class least squares wavelet SVM (LS-WSVM) classifiers is presented, regularization parameters and kernel parameters of LS-WSVM can be optimized. quantum-inspiredevolutionary optimazition can get appropriate parameters of LS-WSVM with global search, so the LS-WSVM model for the multi-class classifiers is built. And then, classification is studied using LS-SVM with wavelet kernel and Gaussian kernel. The simulation results show that the approach for the multi-class LS-WSVM classifiers is effective, that can obtain the optimal parameters of LS-WSVM with global searching QEA, and improved LS-WSVM provides excellent precision for classification.
In this paper, QEA-SOP, a novel evolutionaryalgorithminspired by quantum, is presented to handle the sequential ordering problem (SOP) which is well known as a classical NP-Hard combinatorial proble
In this paper, QEA-SOP, a novel evolutionaryalgorithminspired by quantum, is presented to handle the sequential ordering problem (SOP) which is well known as a classical NP-Hard combinatorial proble
Neural network model of combinational circuit maps the problem of test vector searching to the problem of calculating neural network minimum energy and formulates the question of test generation as a
Neural network model of combinational circuit maps the problem of test vector searching to the problem of calculating neural network minimum energy and formulates the question of test generation as a
Based on a quantum-inspired evolutionary algorithm (QEA), a new disk allocation method is proposed for distributing buckets of a binary cartesian product file among unrestricted number of disks to maximize concurrent ...
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Based on a quantum-inspired evolutionary algorithm (QEA), a new disk allocation method is proposed for distributing buckets of a binary cartesian product file among unrestricted number of disks to maximize concurrent disk I/O. It manages the probability distribution matrix to represent the qualities of the genes. Determining the excellent genes quickly makes the proposed method have faster convergence than DAGA. It gives better solutions and 3.2 - 11.3 times faster convergence than DAGA.
作者:
Li, YangyangZhao, JingjingXidian Univ
Key Lab Intelligent Percept & Image Understanding Minist Educ China Inst Intelligent Informat Proc Xian 710071 Peoples R China
As a global optimizing algorithm, genetic algorithm (GA) is applied to solve the problem of multicast more and more. GA has more powerful searching ability than traditional algorithm, however its property of "pre...
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ISBN:
(纸本)9781424427932
As a global optimizing algorithm, genetic algorithm (GA) is applied to solve the problem of multicast more and more. GA has more powerful searching ability than traditional algorithm, however its property of "prematurity" makes it difficult to get a good multicast tree. A quantum-inspired evolutionary algorithm (QEA) to deal with multicast routing problem is presented in this paper, which saliently solves the "prematurity" problem in Genetic based multicast algorithm. Furthermore, in QEA, the individuals in a population are represented by multistate gene quantum bits and this representation has a better characteristic of generating diversity in population than any other representations. In the individual's updating, the quantum rotation gate strategy is applied to accelerate convergence. The algorithm has the property of simple realization and flexible control. The simulation results show that QEA has a better performance than CS and conventional GA.
inspired by the concept and principles of quantum computing, the classical quantum-inspired evolutionary algorithm (QEA) provides a useful way to find out the approximate solution of many optimization problems. Howeve...
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ISBN:
(纸本)9781479903863
inspired by the concept and principles of quantum computing, the classical quantum-inspired evolutionary algorithm (QEA) provides a useful way to find out the approximate solution of many optimization problems. However, compared with other heuristic algorithms, the slow convergence speed of QEA has been an important issue when it is applied to solve the optimization problems. As such, an improved version, called fast quantum-inspired evolutionary algorithm (FQEA), is proposed in this paper. By adding a fast repair facility, the proposed algorithm can not only accelerate the convergence speed of the search process of QEA, it can also provide a better result than QEA. Experimental results show that the proposed algorithm FQEA can provide a better result than those obtained by QEA and k-means algorithm.
This paper mainly studies the application of a novel Hybrid quantum-inspired evolutionary algorithm (HQEA) called DV HQEA (HQEA based on Differential evolution and Variable neighborhood search) in permutation flow-sho...
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This paper mainly studies the application of a novel Hybrid quantum-inspired evolutionary algorithm (HQEA) called DV HQEA (HQEA based on Differential evolution and Variable neighborhood search) in permutation flow-shop scheduling problem (PFSSP). In this new method, a simple representation method to determine job sequence based on Q-bit's probability amplitude information is proposed for PFSSP firstly. Then the quantum chromosomes are encoded and decoded by using the quantum rotating angle rather than the two probability amplitude strings which are widely adopted in the conventional QEAs. Also, we merge the advantages of Differential Evolution (DE) strategy, Variable Neighborhood Search (VNS) and QEA, and propose the new DV HQEA. By adopting the DE to perform the updating of quantum gate and VNS to perform the local search, we can obtain high performance. Simulation results and comparisons with other algorithms such as NEH heuristic and QEA based on famous benchmarks show the effectiveness of the proposed DV HQEA.
quantum-inspired evolutionary algorithms, one of the three main research areas related to the complex interaction between quantum computing and evolutionaryalgorithms, are receiving renewed attention. A quantum-inspi...
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quantum-inspired evolutionary algorithms, one of the three main research areas related to the complex interaction between quantum computing and evolutionaryalgorithms, are receiving renewed attention. A quantum-inspired evolutionary algorithm is a new evolutionaryalgorithm for a classical computer rather than for quantum mechanical hardware. This paper provides a unified framework and a comprehensive survey of recent work in this rapidly growing field. After introducing of the main concepts behind quantum-inspired evolutionary algorithms, we present the key ideas related to the multitude of quantum-inspired evolutionary algorithms, sketch the differences between them, survey theoretical developments and applications that range from combinatorial optimizations to numerical optimizations, and compare the advantages and limitations of these various methods. Finally, a small comparative study is conducted to evaluate the performances of different types of quantum-inspired evolutionary algorithms and conclusions are drawn about some of the most promising future research developments in this area.
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