In this paper, a hybrid quantum-inspired evolutionary algorithm (HQEA) for Multi-objective JSSP is proposed In the HQEA, a quantum bit is employed to represent processing priority of two operations executed on the sam...
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ISBN:
(纸本)9781424447541
In this paper, a hybrid quantum-inspired evolutionary algorithm (HQEA) for Multi-objective JSSP is proposed In the HQEA, a quantum bit is employed to represent processing priority of two operations executed on the same machine Updating operator of quantum gate is used to speed up individuals converge toward the current best solution Conventional crossover is performed as well However, an individual produced by updating operator and crossover operator may represent no feasible schedule To repair Illegal solution, harmonization algorithm is employed At last, local search operator is designed to exploit the space around the current best solution Experiments are conducted on benchmark test problems, the results show that the proposed approach can search for the near-optimal and non dominated solutions by optimizing the makespan and mean flow time The results of comparisons demonstrates, that the proposed approach outperform another well established multi-objective evolutionaryalgorithm based JSSP approach
This study introduces a quantum-inspired spiking neural network (QiSNN) as an integrated connectionist system, in which the features and parameters of an evolving spiking neural network are optimized together with the...
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This study introduces a quantum-inspired spiking neural network (QiSNN) as an integrated connectionist system, in which the features and parameters of an evolving spiking neural network are optimized together with the use of a quantum-inspired evolutionary algorithm. We propose here a novel optimization method that uses different representations to explore the two search spaces: A binary representation for optimizing feature subsets and a continuous representation for evolving appropriate real-valued configurations of the spiking network. The properties and characteristics of the improved framework are Studied on two different synthetic benchmark datasets. Results are compared to traditional methods, namely a multi-layer-perceptron and a naive Bayesian classifier (NBC). A previously used real world ecological dataset on invasive species establishment prediction is revisited and new results are obtained and analyzed by an ecological expert. The proposed method results in a much faster convergence to an Optimal Solution (or a close to it), in a better accuracy, and in a more informative set of features selected. (C) 2009 Elsevier Ltd. All rights reserved.
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
By leading immune concepts and methods into quantum-inspired evolutionary algorithm (QEA), a novel algorithm, the immune quantum-inspired evolutionary algorithm (IQEA), is proposed. On condition of preserving QEA'...
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ISBN:
(纸本)0780385667
By leading immune concepts and methods into quantum-inspired evolutionary algorithm (QEA), a novel algorithm, the immune quantum-inspired evolutionary algorithm (IQEA), is proposed. On condition of preserving QEA's advantages, IQEA utilizes some characteristics and knowledge in the pending problems for restraining the repeat and ineffective operations during evolution, so as to improve the algorithm efficiency. The experimental results of the knapsack problem show that the performance of IQEA is superior to the conventional EA (CEA), the immune EA (IEA) and QEA.
In this paper, a novel quantum swarm evolutionaryalgorithm (QSE) is presented based on the quantum-inspired evolutionary algorithm (QEA). A new definition of Q-bit expression called quantum angle is proposed, and an ...
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In this paper, a novel quantum swarm evolutionaryalgorithm (QSE) is presented based on the quantum-inspired evolutionary algorithm (QEA). A new definition of Q-bit expression called quantum angle is proposed, and an improved particle swarm optimization (PSO) is employed to update the quantum angles automatically. The simulated results in solving 0-1 knapsack problem show that QSE is superior to traditional QEA. In addition, the comparison experiments show that QSE is better than many traditional heuristic algorithms, such as climb hill algorithm, simulation anneal algorithm and taboo search algorithm. Meanwhile, the experimental results of 14 cities traveling salesman problem (TSP) show that it is feasible and effective for small-scale TSPs, which indicates a promising novel approach for solving TSPs. (c) 2006 Elsevier B.V. All rights reserved.
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.
This paper proposes a novel face verification method using principal components analysis (PCA) and evolutionaryalgorithm (EA). Although PCA related algorithms have shown outstanding performance, the problem lies in m...
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This paper proposes a novel face verification method using principal components analysis (PCA) and evolutionaryalgorithm (EA). Although PCA related algorithms have shown outstanding performance, the problem lies in making decision rules or distance measures. To solve this problem, quantum-inspired evolutionary algorithm (QEA) is employed to find out the optimal weight factors in the distance measure for a predetermined threshold value which distinguishes between face images and non-face images. Experimental results show the effectiveness of the proposed method through the improved verification rate and false alarm rate. (C) 2004 Elsevier B.V. All rights reserved.
Modelling cellular dynamics based on experimental data is at the heart of System Biology. Considerable progress has been made to dynamic pathway modelling as well as the related parameter estimation. However, few of t...
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ISBN:
(纸本)0780384393
Modelling cellular dynamics based on experimental data is at the heart of System Biology. Considerable progress has been made to dynamic pathway modelling as well as the related parameter estimation. However, few of them gives consideration for the issue of optimal sampling time selection for parameter estimation. Time course experiments in molecular biology rarely produce large and accurate data sets and the experiments involved are usually time consuming and expensive. Therefore, to approximate parameters for models with only few available sampling data is of significant practical value. For signal transduction, the sampling intervals are usually not evenly distributed and are based on heuristics. In the paper, we investigate an approach to guide the process of selecting time points in an optimal way to minimize the variance of parameter estimates. In the method, we first formulate the problem to a nonlinear constrained optimization problem by maximum likelihood estimation. We then modify and apply a quantum-inspired evolutionary algorithm, which combines the advantages of both quantum computing and evolutionary computing, to solve the optimization problem. The new algorithm does not suffer from the morass of selecting good initial values and being stuck into local optimum as usually accompanied with the conventional numerical optimization techniques. The simulation results indicate the soundness of the new method.
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