Quantum-inspired Gravitational Search Algorithm Research Articles

How can a hybrid quantum-inspired gravitational search algorithm decrease energy consumption in IoT-based software-defined networks?

The growth of Internet of Things (IoT) devices has prompted the growing use of software-defined networks (SDNs) in today's quickly changing technological environment. In SDN, execution and security of supporting applications and creating an adaptable network design allow the network to associate with applications legitimately. As a result, SDN promotes the growth of IoT-enabled devices, boosts network resource-sharing effectiveness, and boosts the reliability of IoT services. While these interconnected systems offer unprecedented convenience and efficiency, they also come with an increasing energy consumption challenge. The original features of these networks, such as the dynamic topology and energy constraints, challenge the routing issue in these networks. This article delves into the strategies and innovations that can effectively decrease energy consumption in IoT-based SDNs. The previous methods had some problems, such as increasing energy consumption, delay and network lifetime, etc. Thus, fuzzy and meta-heuristic methods have been used to maximize the search space and achieve optimum results. Due to the NP-hard nature of this issue, the Binary Quantum-Inspired Gravitational Search Algorithm (BQIGSA) is used in this paper to offer a fuzzy-based routing approach in IoT-based SDN, which aims to optimize energy, delay, and expected transmission rate. Fuzzy modeling, and particularly fuzzy routing algorithms, are explained in this study in relation to the decision-making component. The synergy of Fuzzy Logic and BQIGSA offers a promising avenue for enhancing IoT-based SDNs. This innovative approach tackles the challenges of uncertainty, energy optimization, and adaptive decision-making that are inherent in IoT networks. The simulation is performed through MATLAB. The outcomes of simulations and tests demonstrated that the suggested approach performed better than the current methods in terms of energy usage, delay rate, and data delivery rate.

Lamb wave-based damage localization and quantification algorithms for CFRP composite structures

Accurately and quantitatively monitoring the damages in complicated composite structures is very challenging, but essential for assessing their integrity. In this paper, an innovative damage monitoring method based on Lamb wave is proposed for large-scale complicated composite laminates. A combination of a resource-constraint Levenberg-Marquardt algorithm associated with a quantum-inspired gravitational search algorithm is introduced to identify the damage location. A damage contour algorithm using convex envelope of damage reflection points and maximum inscribed n-polygon is developed to determine the quantitative damage size. Meanwhile, a singular loci removal scheme based on an automatically updated boundary region constraint and bivariate normal distribution is utilized to separate the correctly positioned points on the damage periphery from those associated with noise. Experiments on two large-scale composite panels with multiple damage cases are conducted to substantiate the proposed algorithms. Sensor layers with embedded piezoelectric transducer (PZT) sensor networks mounted on the structures are employed to excite and receive Lamb wave signals because of their superiority of flexibility, electrical stability and apt for complex structures. The results demonstrate that the proposed technique is capable of pinpointing both location and size of the damage in the complicated composite structures.

Multi-Pose Facial Expression Recognition Using Hybrid Deep Learning Model with Improved Variant of Gravitational Search Algorithm

The recognition of human facial expressions with the variation of poses is one of the challenging tasks in real-time applications such as human physiological interaction detection, intention analysis, marketing interest evaluation, mental disease diagnosis, etc. This research work addresses the problem of expression recognition from different facial poses at the yaw angle. The major contribution of the paper is the proposal of an autonomous pose variant facial expression recognition framework using the amalgamation of a hybrid deep learning model with an improved quantum inspired gravitational search algorithm. The hybrid deep learning model is the integration of the convolutional neural network and recurrent neural network. The applicability of the hybrid deep learning model can be considered as significant if the feature set is efficiently optimized to have the discriminative features respective to each expression class. Here, the Improved Quantum Inspired Gravitational Search Algorithm (IQI-GSA) is utilized for the selection and optimization of features. The IQI-GSA method is significant for optimizing the features compared to quantum-behaved binary gravitation search algorithm for handing the local optima and stochastic characteristics. Comparing with state-of-art techniques, the proposed framework exhibits the outperformed recognition rate for experimentation on Karolinska Directed Emotional Faces (KDEF) and Japanese Female Facial Expression (JAFFE) datasets.

An Improved Quantum-Inspired Gravitational Search Algorithm to Optimize the Facial Features

The optimization of the features is vital to effectively detecting facial expressions. This research work has optimized the facial features by employing the improved quantum-inspired gravitation search algorithm (IQI-GSA). The improvement to the quantum-inspired gravitational search algorithm (QIGSA) is conducted to handle the local optima trapping. The QIGSA is the amalgamation of the quantum computing and gravitational search algorithm that owns the overall strong global search ability to handle the optimization problems in comparison with the gravitational search algorithm. In spite of global searching ability, the QIGSA can be trapped in local optima in the later iterations. This work has adapted the IQI-GSA approach to handle the local optima, stochastic characteristics and maintaining balance among the exploration and exploitation. The IQI-GSA is utilized for the optimized features selection from the set of extracted features using the LGBP (a hybrid approach of local binary patterns with the Gabor filter) method. The system performance is analyzed for the application of automated facial expressions recognition with the classification technique of deep convolutional neural network (DCNN). The extensive experimentation evaluation is conducted on the benchmark datasets of Japanese Female Facial Expression (JAFFE), Radboud Faces Database (RaFD) and Karolinska Directed Emotional Faces (KDEF). To determine the effectiveness of the proposed facial expression recognition system, the results are also evaluated for the feature optimization with GSA and QIGSA. The evaluation results clearly demonstrate the outperformed performance of the considered system with IQI-GSA in comparison with GSA, QIGSA and existing techniques available for the experimentation on utilized datasets.

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.

Parallel Quadri-valent Quantum-Inspired Gravitational Search Algorithm on a heterogeneous platform for wireless sensor networks

Sensor nodes in a wireless sensor Network are assigned for different operational modes to perfume application-specific objectives. The decision to assign operational modes to nodes is a challenging problem in the presence of multiple criteria including energy-efficient, maintaining network connectivity, and fulfilling application goals. Several metaheuristic methods are introduced in the literature to address this NP-hard problem, however, these methods require further improvements in execution-time and finding the optimum solution. In this research, we propose an improved version of a metaheuristic method called Quadri-valent Quantum-Inspired Gravitational Search Algorithm (QQIGSA) to solve Quadri-valent problems by applying a Not Q-Gate and paralleling QQIGSA method on the graphics processing unit. The proposed method employs a heterogeneous platform and justifies its parameters. The experimental results show that the performance enhancement from 1.8 to 2.25 compared to the previous parallel implementations. Moreover, we achieve the speedup of 8 by using the proposed heterogeneous paralleling technique.

An enhanced quantum-inspired gravitational search algorithm for color prediction based on the absorption spectrum

Spot color is widely applied to printing and packing in modern industry, which can satisfy the individualization requirements and express the emotion of products. Color prediction is the core technique for spot color restoration. In this paper, a method that combines the least squares method and gravitation search algorithm is proposed to address the color prediction by using the absorption spectrum. Firstly, the spectral transmittance of the thin film with high transmission and low reflectance characteristics is researched to find the absorbance. Secondly, the least squares method is used to ascertain the primary colors of the spot color. Thirdly, an enhanced quantum gravitation search algorithm is designed to predict the spot color. The predicted results on the 30 spot colors show that the proposed method has higher accuracy in comparison with the three existed methods. The color differences between the prepared spot colors and the reproduced spot colors are all less than 3, in which 75% of the color differences are less 1 and 35% of the color differences are less 0.1. All the results confirm that the proposed method can predict the spot color accurately.

Design optimization of wireless sensor networks in precision agriculture using improved BQIGSA

Using Wireless Sensor Networks (WSNs) is a new direction of research in agricultural and farming applications. Recently, WSNs are widely used in precision agriculture. One of the most important issues in WSNs is improving the energy consumption of the network while the special properties and the connectivity requirements are considered. In this study, a modified binary version of quantum-inspired gravitational search algorithm is developed to optimize the performance of the WSN used in precision agriculture. The proposed approach is applied to a WSN composed of sensor deployed in a farm to monitor the environmental conditions for a precision agriculture. The aim is to determine the operational mode of each sensor to improve the energy consumption and extend the life span of the network, taking into consideration the communication and application-specific requirements. To demonstrate the benefit of our proposed approach, which is called improved BQIGSA, a comparison between this approach and the existing methods including Binary Genetic Algorithm (BGA), Binary Particle Swarm Optimization (BPSO), the Quadrivalent Quantum-Inspired Gravitational Search Algorithm (QQIGSA) and the Binary Quantum-Inspired Gravitational Search Algorithm (BQIGSA) is given. The results of the performed experiments indicate the effectiveness of the WSN designed by the proposed modified BQIGSA in improving the energy consumption and subsequently prolonging the life span of the network.

Unit commitment by an improved binary quantum GSA

Unit commitment (UC) problem is an important optimizing task for scheduling the on/off states of generating units in power system operation over a time horizon such that the power generation cost is minimized. Since, increasing the number of generating units makes it difficult to solve in practice, many approaches have been introduced to solve the UC problem. This paper introduces an improved version of the binary quantum-inspired gravitational search algorithm (BQIGSA) and proposes a new approach to solve the UC problem based on the improved BQIGSA, called QGSA-UC. The proposed approach is applied to unit commitment problems with the number of generating units in the range of 10–120 along with 24-h scheduling horizon and is compared with nine state-of-the-art approaches. Furthermore, four different versions of gravitational approach are implemented for solving the UC problem and compared with those obtained by QGSA-UC. Comparative results clearly reveal the effectiveness of the proposed approach and show that it can be used as a reliable tool to solve UC problem.

Application of binary quantum-inspired gravitational search algorithm in feature subset selection

Feature selection is an important task to improve prediction accuracy of classifiers and to decrease the problem size. Several approaches have been presented to perform feature selection using metaheuristic algorithms. In this paper, we employ the binary quantum-inspired gravitational search algorithm (BQIGSA) combined with the k-nearest neighbor classifier as a wrapper approach to select a (sub-) optimal subset of features. We evaluate the proposed approach on several well-known datasets and compare our approach with other similar state-of-the-art feature selection techniques. Comparative results verify the acceptable performance of the proposed approach in feature selection.

Fuzzy support vector machine based on hyperbolas optimized by the quantum-inspired gravitational search algorithm

Fuzzy support vector machines (FSVMs) are known for their excellent antinoise performance, but there is no general rule when the fuzzy membership function (FMF) is set up. A novel FSVM based on hyperbolas optimized by the quantum-inspired gravitational search algorithm (QGSH-FSVM) is proposed to handle this question. In the proposed QGSH-FSVM, the FMF is defined by two disparate hyperbolas, whose eccentricities are optimized by the quantum-inspired gravitational search algorithm. A variable called diversity, revealing the percentage of a sample in different classes, is proposed to distinguish outliers or noises from valid samples. Experimental results confirm that the QGSH-FSVM is able to provide the best solutions to different situations by optimizing its eccentricities. The traditional support vector machine and the FSVM based on affinity or the distance between a sample and its cluster center, however, can only succeed in some particular problems while failing in others.

QQIGSA: A quadrivalent quantum-inspired GSA and its application in optimal adaptive design of wireless sensor networks

In the recent past, wireless sensor networks (WSN) have been used in a wide variety of applications which have special properties and requirements. In this study, a novel version of quantum-inspired gravitational search algorithm is presented as a new optimization methodology which is well suitable for quadrivalent encoded problems. The proposed method is adapted in a wireless sensor network in precise agriculture application to find an optimal and adaptive design of wireless sensor network to improve the energy consumption and extend the life span of the network, taking into consideration the communication limitations and application-specific requirements. The performance of the proposed method which is called QQIGSA is compared with the binary genetic algorithm (BGA) and binary particle optimization (BPSO). The experimental results indicate that the WSN designed by QQIGSA has the most performance in fulfilling the requirements and prolonging the lifetime of the network.

A quantum-inspired gravitational search algorithm for binary encoded optimization problems

In this paper, a novel population based metaheuristic search algorithm by combination of gravitational search algorithm (GSA) and quantum computing (QC), called a Binary Quantum-Inspired Gravitational Search Algorithm (BQIGSA), is proposed. BQIGSA uses the principles of QC such as quantum bit, superposition and a modified rotation Q-gates strategy together with the main structure of GSA to present a robust optimization tool to solve binary encoded problems. To evaluate the effectiveness of the BQIGSA several experiments are carried out on the combinatorial 0–1 knapsack problems, Max-ones and Royal-Road functions. The results obtained are compared with those of other algorithms including Binary Gravitational Search Algorithm (BGSA), Conventional Genetic Algorithm (CGA), binary particle swarm optimization (BPSO), a modified version of BPSO (MBPSO), a new version of binary differential evolution (NBDE), a quantum-inspired particle swarm optimization (QIPSO), and three well-known quantum-inspired evolutionary algorithms (QIEAs). The comparison reveals that the BQIGSA has merit in the field of optimization.

A quantum inspired gravitational search algorithm for numerical function optimization

Gravitational search algorithm (GSA) is a swarm intelligence optimization algorithm that shares many similarities with evolutionary computation techniques. However, the GSA is driven by the simulation of a collection of masses which interact with each other based on the Newtonian gravity and laws of motion. Inspired by the classical GSA and quantum mechanics theories, this work presents a novel GSA using quantum mechanics theories to generate a quantum-inspired gravitational search algorithm (QIGSA). The application of quantum mechanics theories in the proposed QIGSA provides a powerful strategy to diversify the algorithm’s population and improve its performance in preventing premature convergence to local optima. The simulation results and comparison with nine state-of-the-art algorithms confirm the effectiveness of the QIGSA in solving various benchmark optimization functions.

以量子启发式引力搜寻法作配电系统的虚功率及电压控制

本文是藉由调度配电系统之主变压器有载分接头和电容器来控制虚功率与电压。其主要目的是降低配电系统中的馈电线损失及改善汇流排的电压偏移量，以达到节约能源、并使系统电压能够操作在稳定的范围内。利用虚功率与电压控制设备，加以适当的规划调度，来控制电压及提供虚功率，同时也必须满足限制条件，其限制条件包含主变压器有载分接头及所有电容器一天之中的最大总切换次数需在限制的次数内，及各汇流排的电压需维持在其限定的范围内，以及电力守恒与线路输电容量限制。在满足限制式的条件下，配电网路能供给用户稳定的电压并提升供电品质。本文采用量子启发式引力搜寻法(Quantum-Inspired Gravitational Search Algorithm, QGSA)来作配电系统的虚功率及电压控制问题。量子启发式引力搜寻法是一种以引力搜寻法为基础，并结合量子计算观念与原理，更新解的位置，使其可以有效的搜寻到全域最佳解的算法。为了证实本文所使用的方法有效性，文中以一30个汇流排的配电系统作测试。由结果得知，本文所采用的方法确实能有效的得到令人满意的结果。 This paper investigates reactive power and voltage control in a distribution system by dispatching the main transformer ULTC and capacitors. The main purpose is to reduce the feeder loss and improve the bus voltage profile of the distribution system. The devices of reactive power and voltage control are properly dispatched to control the voltage and to provide reactive power. And the constraints that must be considered include the maximum allowable number of switching operation in a day for ULTC and each capacitor, the bus voltage limit on each bus, the line flow limit on each feeder, and the power conservation limit on each bus. Under satisfying these constraints, the distribution network can provide stable voltage to the consumers, and the quality of power supply can be enhanced. The quantum-inspired gravitational search algorithm (QGSA) is proposed for solving the reactive power and voltage control problem in a distribution system. The QGSA approach is based on the gravitational search algorithm. And the concept and principles of the quantum computing are added to update the solution, so that the global optimal solution can be efficiently found. To confirm the usefulness of the proposed method, a 30-bus distribution system is performed. It is found from the results that the proposed method can effectively get a satisfying solution.