Context: In requirements engineering phase of the software development life cycle, one of the main concerns of software engineers is to select a set of software requirements for implementation in the next release of t...
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Context: In requirements engineering phase of the software development life cycle, one of the main concerns of software engineers is to select a set of software requirements for implementation in the next release of the software from many requirements proposed by the customers, while balancing budget and customer satisfaction. Objective: To analyse the efficacy of quantum-inspired Elitist Multi-objective evolutionary Algorithm (QE-MEA), quantum-inspired Multi-objective Differential Evolution Algorithm (QMDEA) and Multi-objective quantum-inspired Hybrid Differential Evolution (MQHDE) in solving the software requirements selection problem. Method: The paper reports on empirical evaluation of the performance of three quantum-inspired multi objective evolutionaryalgorithms along with Non-dominated Sorting Genetic Algorithm-II (NSGA-II). The comparison includes the obtained Pareto fronts, the three performance metrics - Generational Distance, Spread and Hypervolume, attained boundary solutions, and size of the Pareto front. Results: The results reveal that MQHDE outperformed other methods in producing high quality solutions;while QMDEA is able to produce well distributed solutions with extreme boundary solutions. Conclusion: The hybridization of Differential Evolution with Genetic algorithms coupled with quantum computing concepts (MQHDE) provided a means to effectively balance the two issues of multi-objective optimization - convergence and diversity. (c) 2016 Elsevier B.V. All rights reserved.
Through System Identification techniques, it is possible to obtain a mathematical model for a dynamic system from its input/output data. Due to their intrinsic dynamic behavior and simple and fast training procedure, ...
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Through System Identification techniques, it is possible to obtain a mathematical model for a dynamic system from its input/output data. Due to their intrinsic dynamic behavior and simple and fast training procedure, the use of echo state networks (ESNs), a kind of neural network, for System Identification is advantageous. However, ESNs have global parameters that should be tuned in order to improve their performance in a determined task. Besides, a random reservoir may not be ideal in terms of performance. Due to their theoretical ability to obtain good solutions with few evaluations, the Real Coded quantum-inspiredevolutionary Algorithm (QIEA-R) and the Covariance Matrix Adaptation Evolution Strategy (CMA-ES) represent efficient alternatives of evolutionaryalgorithms for optimizing ESN global parameters and weights. Thus, this work proposes a neuro-evolutionary method that automatically defines an ESN for System Identification problems. The method initially focuses on finding the best ESN global parameters by using the QIEA-R or the CMA-ES then, in sequence, selecting its best reservoir, which can be done by a second optimization focused on some reservoir weights or by doing a simple choice based on networks with random reservoirs. The method was applied to seven benchmark problems in System Identification produced good results when compared to traditional methods.
Semantic segmentation can be applied to a wide range of applications. One of the most interesting is medical image analysis. Applying semantic segmentation techniques in that field has great potential to assist physic...
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ISBN:
(纸本)9781665488679
Semantic segmentation can be applied to a wide range of applications. One of the most interesting is medical image analysis. Applying semantic segmentation techniques in that field has great potential to assist physicians in diagnosing and analysing medical scans for the patient's benefit. Traditionally, several techniques have been applied in order to perform semantic segmentation. However, with the development of Deep Learning methods, there was a paradigm shift. Deep learning techniques are able to achieve great results, comparable to human-level performance. In order to achieve state-of-the-art results, researchers have to lean on the task of designing novel deep neural network architectures. That process is very time-consuming and heavily relies on experience and expert knowledge. Neural architecture search is the process of automatising the search for new deep neural network architectures. quantum-inspired Neural Architecture Search is a neural architecture search algorithm that leverages the benefits of quantum-inspired computing to search for neural network architectures efficiently. In this work, we adapt this to search for semantic segmentation neural networks and apply it to medical image analysis. The Spleen and Prostate datasets from the Medical Segmentation Decathlon challenge were used. Results show that our work was able to find better-performing semantic segmentation architectures for both datasets: 0.9583 +/- 0.006 in comparison to ResU-Net 0.9525 +/- 0.008 for the spleen dataset, and 0.6887 +/- 0.067 in comparison to 0.6529 +/- 0.070 for the prostate dataset.
quantumalgorithms, based on the principles of quantum mechanics, offer significant parallel processing capabilities with a wide range of applications. Nature-inspired stochastic optimization algorithms have long been...
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quantumalgorithms, based on the principles of quantum mechanics, offer significant parallel processing capabilities with a wide range of applications. Nature-inspired stochastic optimization algorithms have long been a research hotspot. The fusion of quantum mechanics with optimization methods can potentially address NP-hard problems more efficiently and exponentially faster. The potential advantages provided by the ground-breaking paradigm have expedited the scientific output of quantum-inspired optimization algorithms locale. Consequently, a pertinent investigation is required to explain how ground-breaking scientific advancements have evolved. The scientometric approach utilizes quantitative and qualitative techniques to analyze research publications to evaluate the structure of scientific knowledge. Henceforth, the current research presents a scientometric and systematic analysis of quantum-inspired metaheuristic algorithms (QiMs) literature from the Scopus database since its inception. The scientometric implications of the article offer a detailed exploration of the publication patterns, keyword co-occurrence network analysis, author co-citation analysis and country collaboration analysis corresponding to each opted category of QiMs. The analysis reveals that QiMs solely account to 26.66% of publication share in quantum computing and have experienced an impressive 42.59% growth rate in the past decade. Notably, power management, adiabatic quantum computation, and vehicle routing are prominent emerging application areas. An extensive systematic literature analysis identifies key insights and research gaps in the QiMs knowledge domain. Overall, the findings of the current article provide scientific cues to researchers and the academic fraternity for identifying the intellectual landscape and latest research trends of QiMs, thereby fostering innovation and informed decision-making.
The Resource-Constrained Project Scheduling Problem (RCPSP) is an NP-hard optimisation problem that can be found in many real-world applications. Considerable research effort has been put into overcoming the difficult...
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ISBN:
(纸本)9781728183923
The Resource-Constrained Project Scheduling Problem (RCPSP) is an NP-hard optimisation problem that can be found in many real-world applications. Considerable research effort has been put into overcoming the difficulties in solving the RCPSP by proposing innovative heuristics, meta-heuristics and their hybridisation. However, finding optimal solutions is still not guaranteed. It is known that quantum-inspired meta-heuristics can improve population diversity and the quality of solutions but little has been published on adapting them to solving RCPSPs. Here, we examine the performance of a quantum-inspired Differential Evolution (QIDE) algorithm in solving such problems. The proposed QIDE uses a quantum population that is initialised using the rotation quantum gate and quantum superposition in the continuous domain, and then evolved using the differential-evolution operators. A local search is also adopted to accelerate convergence. The performance of the QIDE algorithm was tested by solving problems with 30 and 60 activities from the PSPLIB benchmark datasets. The QIDE algorithm outperformed another quantum-based particle swarm algorithm and some other meta-heuristics.
This work presents a new model for the automatic synthesis of fuzzy classifiers, based on quantum-inspired evolutionary algorithms, which overcomes the difficulties inherent to the use of hybrid representations and th...
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This work presents a new model for the automatic synthesis of fuzzy classifiers, based on quantum-inspired evolutionary algorithms, which overcomes the difficulties inherent to the use of hybrid representations and the treatment of multiple objectives, both necessary for the synthesis of these types of systems. Without any a priori information about the classifier rules or any initial adjustment of individuals, the results obtained are comparable to those of other techniques that start from classifier populations previously adjusted to obtain good performance. According to the current trend, the aim was to build classifiers with good accuracy and simultaneous high interpretability of their fuzzy rule base.
In Brain-Computer Interfaces, one of the most relevant tasks is the selection of a subset of features that efficiently describes the EEG signal, excluding redundant and irrelevant features. This procedure reduces the ...
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ISBN:
(纸本)9781509060177
In Brain-Computer Interfaces, one of the most relevant tasks is the selection of a subset of features that efficiently describes the EEG signal, excluding redundant and irrelevant features. This procedure reduces the dimensionality of the dataset (avoiding the dimensionality curse) and improves the classification accuracy of the system. One of the most successful models applied for this task is the use of an evolutionary Algorithm in a wrapper approach. These models produce excellent results but present the drawback of a considerable high processing time, a critical limitation for its application on real Brain-Computer Interfaces (BCI) systems. quantum-inspired evolutionary algorithms can be an alternative wrapper approach for the feature selection task, given that they outperform classical evolutionaryalgorithms in the exploration and exploitation of the search space, obtaining the global solution much faster. These algorithm employs concepts and principles from the quantum Mechanics to probabilistically describe a set of different states between the classical logic states 0 and 1. In this paper, a quantum-inspiredevolutionary Algorithm is developed and tested over three different subjects from publicly available datasets. In the proposed model, Wavelet Packet Decomposition is employed to analyze the time-frequency characteristics of the signals, and a Multilayer Perceptron Neural Network is employed as a classifier.
In many complex manufacturing environments, the running equipment must be monitored by Wireless Sensor Networks (WSNs), which not only requires WSNs to have long service lifetimes, but also to achieve rapid and high-q...
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In many complex manufacturing environments, the running equipment must be monitored by Wireless Sensor Networks (WSNs), which not only requires WSNs to have long service lifetimes, but also to achieve rapid and high-quality transmission of equipment monitoring data to monitoring centers. Traditional routing algorithms in WSNs, such as Basic Ant-Based Routing (BABR) only require the single shortest path, and the BABR algorithm converges slowly, easily falling into a local optimum and leading to premature stagnation of the algorithm. A new WSN routing algorithm, named the quantum Ant Colony Multi-Objective Routing (QACMOR) can be used for monitoring in such manufacturing environments by introducing quantum computation and a multi-objective fitness function into the routing research algorithm. Concretely, quantum bits are used to represent the node pheromone, and quantum gates are rotated to update the pheromone of the search path. The factors of energy consumption, transmission delay, and network load-balancing degree of the nodes in the search path act as fitness functions to determine the optimal path. Here, a simulation analysis and actual manufacturing environment verify the QACMOR's improvement in performance.
quantum-inspired evolutionary algorithms (QIEA) represent an efficient alternative to the traditional genetic algorithms, being capable of finding good solutions with smaller populations. Echo State Networks (ESNs) ar...
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ISBN:
(纸本)9781509060177
quantum-inspired evolutionary algorithms (QIEA) represent an efficient alternative to the traditional genetic algorithms, being capable of finding good solutions with smaller populations. Echo State Networks (ESNs) are a simple and efficient implementation of the Reservoir Computing framework. The use of this kind of networks in system identification is advantageous due to its intrinsic dynamic behavior and fast training procedure. However, ESNs have global parameters that should be tuned in order to improve their performance in a determined task. Besides, the random generation of the reservoir weights of these networks may not be ideal in terms of performance. Thus, this work presents a method that automatically defines an ESN for system identification problems by using a real coded QIEA (QIEA-R) in a two-phase optimization procedure. The QIEA-R firstly searches for the best global parameters of an ESN;then, on a second stage, optimizes some of its reservoir weights. In two benchmark problems for system identification, the proposed method overcame the performance of a randomly generated ESN with the same global parameters and has presented comparable and, in most cases, better accuracy results in comparison to some methods which were applied to the same datasets.
FP-AK-QIEA-R has been used in Benchmark Functions, Protein Folding Problem and multi-objective problems. PECO was a proposed algorithm with good performance in Protein Folding Problem. The objective of this paper is p...
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ISBN:
(纸本)9781538654903
FP-AK-QIEA-R has been used in Benchmark Functions, Protein Folding Problem and multi-objective problems. PECO was a proposed algorithm with good performance in Protein Folding Problem. The objective of this paper is perform experiments to improve the performance of PECO using FP-AK-QIEA-R to initialize PECO. After plotting the best of ever, a meaningful change on the convergence is observed showing the modification of the convergence and after plotting the number of habitats is noticed a reduction of the number of habitats. In conclusion, adding FP-AK-QIEA-R solution improves the performance of PECO.
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