Quantum-inspired Genetic Algorithm Research Articles

Stabilization analysis for nonlinear interconnected system with memory based coupled sampled data control via quantum-inspired genetic algorithm

In the realm of modern interconnected systems, achieving stability represents a pivotal challenge due to the complex dynamics among various components. This paper presents a new approach to stability analysis, leveraging linear matrix inequalities and Lyapunov-Krasovskii functionals. The framework of memory-based coupling sampled data control is enhanced by a quantum genetic algorithm. By incorporating linear matrix inequality approach, this study establishes a robust mathematical foundation for characterizing stability conditions and synthesizing control gains. This innovative integration provides a powerful toolset to optimize control parameters while mitigating the impact of disturbances. The simulation findings are explicitly based on experimental values of two-area interconnected power systems with doubly fed induction generator based wind farm, which guarantees the asymptotic stability of the proposed controller.

Cost-aware quantum-inspired genetic algorithm for workflow scheduling in hybrid clouds

Cloud computing delivers a desirable environment for users to run their different kinds of applications in a cloud. Numerous of these applications (tasks), such as bioinformatics, astronomy, biodiversity, and image analysis, are deadline-sensitive. Such tasks must be properly allocated to virtual machines (VMs) to avoid deadline violations, and they should reduce their execution time and cost. Due to the contradictory environment, minimizing the application task's completion time and execution cost is extremely difficult. Thus, we propose a Cost-aware Quantum-inspired Genetic Algorithm (CQGA) to minimize the execution time and cost by meeting the deadline constraints. CQGA is motivated by quantum computing and genetic algorithm. It combines quantum operators (measure, interference, and rotation) with genetic operators (selection, crossover, and mutation). Quantum operators are used for better population diversity, quick convergence, time-saving, and robustness. Genetic operators help to produce new individuals, have good fitness values for individuals, and play a significant role in preserving the evolution quality of the population. In addition, CQGA used a quantum bit as a probabilistic representation because it has higher population diversity attributes than other representations. The simulation outcome exhibits that the proposed algorithm can obtain outstanding convergence performance and reduced maximum cost than benchmark algorithms.

Multipath traveling purchaser problem with time-dependent market structure using quantum-inspired variable length genetic algorithm

In developing countries, rural and semi-urban markets remain open for a particular period of time in a day, and items’ availabilities and prices vary within the opening hours of a market. Nowadays, the Internet of Things (IoT) and Type-2 fuzzy logic (T2FL) systems are used to make real-time decisions. With the logistical and infrastructural development around the world, there are several alternate routes for travel between different markets and cities. Taking the above realities into consideration, here, we develop and analyze IoT-enabled and T2FL system-based multipath traveling purchaser problems with time-dependent market structure (IoT-T2FL-MPTPPswTDMS). In this study, information regarding weather, road surface, and congestion concerning different paths obtained by the IoT are used as inputs in T2FL, and path-wise, the vehicle’s average velocity is obtained using the fuzzy rules. Item availability and price in a particular market change with time. The optimization problem is, for different journey starting times, to select the appropriate markets for the purchase and the corresponding optimum routing path for minimum cost (time) against time (cost) constraint. A multi parent, varied-offspring, variable length quantum-inspired genetic algorithm (MPVOVLQiGA) with quantum selection and mutation and multi parent, varied-offspring crossover is developed to solve the proposed problems. For children, mimicking the real-life parental system, In vitro fertilization (IVF) and adopted child (if IVF failed) are considered alongwith the usual comparison crossover. The algorithm’s superiority is established through a statistical test. A real-life model is numerically illustrated. The dependence on the minimum cost and time on the journey’s commencement time is demonstrated.

A Modified Quantum-Inspired Genetic Algorithm Using Lengthening Chromosome Size and an Adaptive Look-Up Table to Avoid Local Optima

The quantum-inspired genetic algorithm (QGA), which combines quantum mechanics concepts and GA to enhance search capability, has been popular and provides an efficient search mechanism. This paper proposes a modified QGA, called dynamic QGA (DQGA). The proposed algorithm utilizes a lengthening chromosome strategy for a balanced and smooth transition between exploration and exploitation phases to avoid local optima and premature convergence. Apart from that, a novel adaptive look-up table for rotation gates is presented to boost the algorithm’s optimization abilities. To evaluate the effectiveness of these ideas, DQGA is tested by various mathematical benchmark functions as well as real-world constrained engineering problems against several well-known and state-of-the-art algorithms. The obtained results indicate the merits of the proposed algorithm and its superiority for solving multimodal benchmark functions and real-world constrained engineering problems.

A memetic quantum-inspired genetic algorithm based on tabu search

A memetic quantum-inspired genetic algorithm based on tabu search

An Efficient QGA-based Model for Resource Allocation in D2D Communication for 5G-HCRAN Networks

Device-to-Device (D2D) communication with Cloud Radio Access Networks (CRAN) has emerged as a promising technology to fulfil the objective of 5th-Generation networks with constrained bandwidth. The CRAN system reduces capital and operation expenditure and improves network throughput. In D2D-CRAN resource allocation, the D2D users reuse the resource block of the cellular user (CU), which tends to interfere; therefore, it breaks down the system's data rate. Interference mitigation is one of the biggest challenges in the D2D-CRAN communication system. The mitigation techniques are used for interference, and a resource allocation scheme based on a Quantum-Inspired Genetic algorithm (QGA) is proposed. This resource allocation scheme combines both the quantum computing mechanism and genetic algorithm for the allocation of a resource block to cellular and D2D users. Initially, we figure out the low complexity allocation scheme based on the calculated D2D throughput. Furthermore, QGA is employed in the population matrix to find the best approach to allocate CU’s resources to several D2D users. The simulation output illustrates that the suggested scheme outperforms existing methods.

QUANTUM INSPIRED GENETIC ALGORITHM FOR BI-LEVEL THRESHOLDING OF GRAY-SCALE IMAGES

Abstract. Thresholding is the primitive step in the process of image segmentation. Finding the optimal threshold for satellite images with reduced computation time and resources is still a challenging task. In this paper, we propose a Grey-Level Co-occurrence Matrix based Quantum Inspired Genetic Algorithm (QGA-GLCM) for bi-level thresholding of gray-scale images (natural and satellite). In this paper, QGA was used to find the optimal threshold. The results are compared with four different variants of Differential Evolution (DE) meta-heuristic algorithms, namely- DE-Otsu, DE-Kapur, DE-Tsali’s, DE-GLCM, and three different variants of QGA, namely- QGA-Otsu, QGA-Kapur, QGA-Tsali’s. Intensity value from image pixel is the only information used by Otsu, Tsali’s and Kapur for thresholding and are highly affected by noise. The main objective of this paper was a) To have a binary threshold for images corrupted with noise by bringing in spatial context b) To reduce the computational complexity and time for generating a threshold. Performance evaluators viz., CPU time, Peak Signal-to-Noise Ratio (PSNR), Mean Square Error (MSE), and Structural Similarity Index Measure (SSIM) were used for quantitative assessment of partitioned images. From this study we observed that our proposed technique, QGA-GLCM is a) very good at producing a diverse population b) ten times faster than its classical counterparts c) generates better threshold for images corrupted by noise. In general, the threshold values generated by QGA and its variants are better than its classical counterparts. The results clearly show that exploration and exploitation capability of QGA is superior to DE for all variants. QGA-GLCM can be an effective technique to generate thresholds both in terms of computational speed and time.

PP-DDP: a privacy-preserving outsourcing framework for solving the double digest problem

BackgroundAs one of the fundamental problems in bioinformatics, the double digest problem (DDP) focuses on reordering genetic fragments in a proper sequence. Although many algorithms for dealing with the DDP problem were proposed during the past decades, it is believed that solving DDP is still very time-consuming work due to the strongly NP-completeness of DDP. However, none of these algorithms consider the privacy issue of the DDP data that contains critical business interests and is collected with days or even months of gel-electrophoresis experiments. Thus, the DDP data owners are reluctant to deploy the task of solving DDP over cloud.ResultsOur main motivation in this paper is to design a secure outsourcing computation framework for solving the DDP problem. We at first propose a privacy-preserving outsourcing framework for handling the DDP problem by using a cloud server; Then, to enable the cloud server to solve the DDP instances over ciphertexts, an order-preserving homomorphic index scheme (OPHI) is tailored from an order-preserving encryption scheme published at CCS 2012; And finally, our previous work on solving DDP problem, a quantum inspired genetic algorithm (QIGA), is merged into our outsourcing framework, with the supporting of the proposed OPHI scheme. Moreover, after the execution of QIGA at the cloud server side, the optimal solution, i.e. two mapping sequences, would be transferred publicly to the data owner. Security analysis shows that from these sequences, none can learn any information about the original DDP data. Performance analysis shows that the communication cost and the computational workload for both the client side and the server side are reasonable. In particular, our experiments show that PP-DDP can find optional solutions with a high success rate towards typical test DDP instances and random DDP instances, and PP-DDP takes less running time than DDmap, SK05 and GM12, while keeping the privacy of the original DDP data.ConclusionThe proposed outsourcing framework, PP-DDP, is secure and effective for solving the DDP problem.

A Modified Quantum-Inspired Genetic Algorithm for Continuum Structural Topology Optimization

Topology optimization and quantum computing have evolved rapidly over the past three decades. Previous topological optimum design methods suffered from financial burden and mathematical complexity. To overcome these shortcomings, a modified quantum-inspired evolutionary algorithm-based topology optimization method is proposed. This nested approach combines the classic solid isotropic microstructure with the penalization method and the double chains quantum genetic algorithm to establish an integral topology optimization framework. The former is utilized to determine the search direction of design variable updating. Meanwhile, the latter ensures abundant search diversity. The validity and feasibility of the developed methodology are eventually demonstrated by several application examples. The results indicate that the proposed optimization framework is independent of initial values and can lead to optimized structures. In addition, it will be more appropriate and effective if this strategy is deployed on a quantum computer in the future.

A multi-objective quantum-inspired genetic algorithm for workflow healthcare application scheduling with hard and soft deadline constraints in hybrid clouds

Recently, the use of quantum cloud computing for different applications has been increasing. For instance, weather forecasting, financial modeling, healthcare, and automation are geographically distributed in practice. These applications are workflows and consist of compute-intensive dependent tasks with precedence constraints. However, workflow processing on quantum-based cloud services still faces issues in the literature regarding makespan and energy consumption. This study presents the Multi-objective Quantum-inspired Genetic Algorithm (MQGA) to address the problems of workflow scheduling in the hybrid cloud, attempting to reduce makespan and energy consumption simultaneously. The proposed algorithm relies on the concept and principle of quantum mechanics, which explores the computational power of quantum computing. It adopted a qubit to represent the individual chromosome for better population diversity. It also uses a quantum rotation gate to lead the schedule to better convergence and avoids classical genetic operators. The simulation results show that the proposed algorithm can effectively reduce energy consumption by 23.36% and makespan 20% on average.

Joint optimization of inspection and maintenance strategy for complex multi-component systems using a quantum-inspired genetic algorithm

Advances in sensor and data technology enable real-time condition monitoring, thus extending the opportunities for condition-based maintenance (CBM) to be applied in practice. In this paper, a joint inspection and maintenance strategy for multi-component systems is proposed. The objective of this strategy is to minimize the long-run expected operational cost by jointly considering the inspection frequency of each health monitor in the system and the threshold for the maintenance initialization. To find the optimal strategy, a dynamic Bayesian network-based maintenance model is developed at first to provide reasoning of the dynamic reliability of degrading components in the multi-component system, in which complex relationship among inspections by different health monitors, different failure modes in the system, and different maintenance actions to system components are considered and quantified. Then, a quantum-inspired genetic algorithm (QGA) is proposed to optimize the strategy. With quantum encoding method, improved rotation gate, and specially designed crossover and mutation operators, the QGA is able to find the optimal strategy for multi-component systems with a general system structure. An example simplified from real practice is presented to demonstrate the effectiveness and advantages of the proposed strategy and the optimization algorithm, with comparison to similar strategies and traditional intelligent optimization algorithms.

A novel quantum inspired genetic algorithm to initialize cluster centers in fuzzy C-means

We propose a new quantum-inspired genetic algorithm (QGA) aimed to find the optimal initial cluster centers of the Fuzzy C-Means (FCM). The quantum chromosomes of the quantum population are collapsed to conventional chromosomes formed by binary strings encoding the cluster centers. We set as fitness function to minimize a fuzzy entropy function used to measure the fuzziness of clustering. A quantum rotation gate operator is applied in any generation to evolve the quantum population. The best conventional chromosome is used to set the initial cluster centers running FCM. We execute comparative tests of our QGA with other quantum-inspired method proposed in literature in order to find the best initial cluster centers: the results show that our method provides the best trade-off between the accuracy and precision of results and execution times.

Quantum-inspired meta-heuristic algorithms with deep learning for facial expression recognition under varying yaw angles

In recent years, the increasing human–computer interaction has spurred the interest of researchers towards facial expression recognition to determine the expressive changes in human beings. The detection of relevant features that describe the expressions of different individuals is vital to describe human expressions accurately. The present work has employed the integrated concept of Local Binary Pattern and Histogram of Gradient for facial feature extraction. The major contribution of the paper is the optimization of the extracted features using quantum-inspired meta-heuristic algorithms of QGA (Quantum-Inspired Genetic Algorithm), QGSA (Quantum-Inspired Gravitational Search Algorithm), QPSO (Quantum-Inspired Particle Swarm Optimization), and QFA (Quantum-Inspired Firefly Algorithm). These quantum-inspired meta-heuristic algorithms utilize the attributes of quantum computing that ensure the adequate control of facial feature diversity with quantum measures and Q-bit superstition states. The optimized features are fed to the deep learning (DL) variant deep convolutional neural network added with residual blocks (DCNN-R) for the classification of expressions. The facial expressions are detected for the KDEF and RaFD datasets under varying yaw angles of –90∘, –45∘, 0∘, 45∘, and 90∘. The detection of facial expressions with varying angles is also a crucial contribution, as the features decrease with the increasing yaw angle movement of the face. The experimental evaluations demonstrate the superior performance of the QFA than other algorithms for feature optimization and hence the better classification of facial expressions.

Quantum Inspired Genetic Algorithm Model based Automatic Modulation Classification

The popularity of automatic modulation categorization (AMC) is high in recent years owing to the many advantages. When it comes to communication, reliability in an AMC is very critical. Increasing the amount of signals exponentially increases the cost of using the AMC. Precise classification methods, such as neural network, in which either the parameters of the neural network or the dimensions of the input or output variables are modified dynamically, are not successful in obtaining high accuracy results. To improve the accuracy of the modulation categorization, this study employs a "QIGA" feature selection model based on Quantum (inspired) Genetic Algorithm (QIGA). QIGA is used to choose the correct functionality, and to limit the number of examples that must be learned so that the overall system time is shortened and the cost of computing is reduced. Selecting excellent characteristics is enhanced via quantum computing, and this is done to lower the complexity of the solutions. The internal validation results demonstrated that the QIGA model significantly improved the statistical match quality and significantly outperformed the other models.

A Quantum-Inspired Genetic Algorithm for Extractive Text Summarization

Automatic text summarization has recently become a key instrument for reducing the huge quantity of textual data. In this paper, the authors propose a quantum-inspired genetic algorithm (QGA) for extractive single-document summarization. The QGA is used inside a totally automated system as an optimizer to search for the best combination of sentences to be put in the final summary. The presented approach is compared with 11 reference methods including supervised and unsupervised summarization techniques. They have evaluated the performances of the proposed approach on the DUC 2001 and DUC 2002 datasets using the ROUGE-1 and ROUGE-2 evaluation metrics. The obtained results show that the proposal can compete with other state-of-the-art methods. It is ranked first out of 12, outperforming all other algorithms.

A novel extractive multi-document text summarization system using quantum-inspired genetic algorithm: MTSQIGA

The explosive growth of textual data on the web and the problem of obtaining desired information through this enormous volume of data has led to a dramatic increase in demand for developing automatic text summarization systems. For this reason, this paper presents a novel multi-document text summarization approach, called MTSQIGA, which extracts salient sentences from source document collection to generate the summary. The proposed generic summarizer models extractive summarization as a binary optimization problem that applies a modified quantum-inspired genetic algorithm (QIGA) in its processing stage to find the best solution. Objective function of our approach plays an important role in optimizing linear combination of coverage, relevance, and redundancy factors which consists of six sentence scoring measures. To ensures the generation of a summary with predefined length limit, the presented QIGA employs a modified quantum measurement and a self-adaptive quantum rotation gate based on the quality and length of the summary. Evaluation of the proposed system was performed on DUC 2005 and 2007 benchmark datasets in terms of ROUGE standard measures. Comparison of MTSQIGA with existing state-of-the-art approaches for multi-document summarization shows superior performance of the proposed systems over other methods on both existing benchmark datasets. It also indicates promising efficiency of our proposed algorithm on applying quantum-inspired genetic algorithm to the text summarization tasks.

Quantum-Inspired Genetic Algorithm for Resource-Constrained Project-Scheduling

The Resource-Constrained Project-Scheduling Problem (RCPSP) is an NP-hard problem which can be found in many research domains. The optimal solution of the RCPSP problems requires a balance between exploration/exploitation and diversification/intensification. With this in mind, quantum-inspired evolutionary algorithms' ability to improve the population and quality of solutions, this work investigates the performance of a quantum-inspired genetic algorithm (QIGA), which has been adapted to work with RCPSPs. The proposed QIGA possesses the same structure as classical genetic algorithms, but the initial and updated populations are implemented using quantum gates and quantum superposition, bearing in mind the adaptation of such operators to fit with discrete problems. This work aims to solve RCPSPs using the QIGA to investigate the influence of the various quantum parameters in the proposed algorithm, such as the quantum population size, different options for the quantum gates and re-combination to use in the QIGA. The well-known PSPLIB benchmark instances of J30, J60 and J120 activities are used to test the effectiveness and performance of the proposed QIGA. It is apparent from the results that quantum mutation, quantum crossover and representation of quantum superposition using quantum gates enhances population diversity. The QIGA is also found to outperform many other evolutionary algorithms.

A Structural Evolution-Based Anomaly Detection Method for Generalized Evolving Social Networks

Abstract Recently, text-based anomaly detection methods have obtained impressive results in social network services, but their applications are limited to social texts provided by users. To propose a method for generalized evolving social networks that have limited structural information, this study proposes a novel structural evolution-based anomaly detection method ($SeaDM$), which mainly consists of an evolutional state construction algorithm ($ESCA$) and an optimized evolutional observation algorithm ($OEOA$). $ESCA$ characterizes the structural evolution of the evolving social network and constructs the evolutional state to represent the macroscopic evolution of the evolving social network. Subsequently, $OEOA$ reconstructs the quantum-inspired genetic algorithm to discover the optimized observation vector of the evolutional state, which maximally reflects the state change of the evolving social network. Finally, $SeaDM$ combines $ESCA$ and $OEOA$ to evaluate the state change degrees and detect anomalous changes to report anomalies. Experimental results on real-world evolving social networks with artificial and real anomalies show that our proposed $SeaDM$ outperforms the state-of-the-art anomaly detection methods.

Quantum­-Inspired Genetic Algorithm for Solving the Test Suite Minimization Problem

Test Suite Minimization problem is a nondeterministic polynomial time (NP) complete problem insoftware engineering that has a special importance in software testing. In this problem, a subset with a minimalsize that contains a number of test cases that cover all the test requirements should be found. A brute­forceapproach to solving this problem is to assume a size for the minimal subset and then search to find if there is asubset of test cases with the assumed size that solves the problem. If not, the assumed minimal size is graduallyincremented, and the search is repeated. In this paper, a quantum­inspired genetic algorithm (QIGA) will beproposed to solve this problem. In it, quantum superposition, quantum rotation and quantum measurement willbe used in an evolutionary algorithm. The paper will show that the adopted quantum techniques can speed upthe convergence of the classical genetic algorithm. The proposed method has an advantage in that it reduces theassumed minimal number of test cases using quantum measurements, which makes it able to discover the minimalnumber of test cases without any prior assumptions.

Imprecise modified solid green traveling purchaser problem for substitute items using quantum-inspired genetic algorithm

This paper develops a modified formulation of the traveling purchaser problem (TPP), in capacitated markets with the availability of both original and substitute products where purchaser focuses on minimizing an imprecise procurement cost using multiple vehicle types. We also develop an environment-friendly green procurement planning through suitable penalty/credit structure. The motivation for this study emerges from a hub and spoke procurement planning model, with a focus on environmental concerns following regulations and customer sentiments. To obtain the solution with faster execution time, a quantum-inspired genetic algorithm with in vitro fertilization (IVF) crossover and mutation operator is developed. Our results illustrate the non-linear change in the number of markets selection, routing and procurement planning with changes in product availability and substitutes. We show that the designing of suitable carbon credit/penalty structure is essential to obtain a green network with minor compromises in travel and transportation costs. The flexibility of multiple vehicle types reduces both cost and emission significantly. As a part of the methodological contribution, the superiority of our heuristic is established by testing on benchmark TPP instances using a rigorous statistical test.