Quantum-inspired Particle Swarm Optimization Research Articles

Predicting air quality index using attention hybrid deep learning and quantum-inspired particle swarm optimization

Air pollution poses a significant threat to the health of the environment and human well-being. The air quality index (AQI) is an important measure of air pollution that describes the degree of air pollution and its impact on health. Therefore, accurate and reliable prediction of the AQI is critical but challenging due to the non-linearity and stochastic nature of air particles. This research aims to propose an AQI prediction hybrid deep learning model based on the Attention Convolutional Neural Networks (ACNN), Autoregressive Integrated Moving Average (ARIMA), Quantum Particle Swarm Optimization (QPSO)-enhanced-Long Short-Term Memory (LSTM) and XGBoost modelling techniques. Daily air quality data were collected from the official Seoul Air registry for the period 2021 to 2022. The data were first preprocessed through the ARIMA model to capture and fit the linear part of the data and followed by a hybrid deep learning architecture developed in the pretraining–finetuning framework for the non-linear part of the data. This hybrid model first used convolution to extract the deep features of the original air quality data, and then used the QPSO to optimize the hyperparameter for LSTM network for mining the long-terms time series features, and the XGBoost model was adopted to fine-tune the final AQI prediction model. The robustness and reliability of the resulting model were assessed and compared with other widely used models and across meteorological stations. Our proposed model achieves up to 31.13% reduction in MSE, 19.03% reduction in MAE and 2% improvement in R-squared compared to the best appropriate conventional model, indicating a much stronger magnitude of relationships between predicted and actual values. The overall results show that the attentive hybrid deep Quantum inspired Particle Swarm Optimization model is more feasible and efficient in predicting air quality index at both city-wide and station-specific levels.

Combined emission economic dispatch using quantum-inspired particle swarm optimization and its variants

The ever-increasing electricity demand, its dependency on fossil fuels, and the consequent environmental degradation are major concerns of this era. The worldwide domination of fossil fuels in bulk electricity generation is rapidly increasing the emissions of CO[Formula: see text] and other environmentally dangerous gases that are contributing to climate change. The economic and emission dispatch are two important problems in thermal power generation whose combination produces a complex highly constrained nonlinear optimization problem known as combined economic and emission dispatch. The optimization of combined economic and emission dispatch aims to allocate the generation of committed units to minimize fuel cost and emissions, simultaneously while honoring all equality and inequality constraints. Therefore, in this article, we investigate a solution of the combined economic and emission dispatch problem using quantum particle swarm optimization and its two modified versions, that is, enhanced quantum particle swarm optimization and quantum particle swarm optimization integrated with weighted mean personal best and adaptive local attractor. The enhanced quantum particle swarm optimization algorithm achieves particles’ diversification at early stages and shows good performance in local search at later stages. The quantum particle swarm optimization integrated with weighted mean personal best and adaptive local attractor boosts search performance of quantum particle swarm optimization and attains better global optimality. The suggested methods are employed to achieve solution for the combined economic and emission dispatch in four distinct systems, encompassing two scenarios with 6 units each, one with a 10-unit configuration, and another with an 11-unit setup. A comparative analysis with methodologies documented in existing literature reveals that the proposed approach outperforms others, demonstrating superior computational performance and robust efficiency.

An Improved Quantum Inspired Particle Swarm Optimization for Forest Cover Prediction

An Improved Quantum Inspired Particle Swarm Optimization for Forest Cover Prediction

Reinforcement Learning vs. Computational Intelligence: Comparing Service Management Approaches for the Cloud Continuum

Modern computing environments, thanks to the advent of enabling technologies such as Multi-access Edge Computing (MEC), effectively represent a Cloud Continuum, a capillary network of computing resources that extend from the Edge of the network to the Cloud, which enables a dynamic and adaptive service fabric. Efficiently coordinating resource allocation, exploitation, and management in the Cloud Continuum represents quite a challenge, which has stimulated researchers to investigate innovative solutions based on smart techniques such as Reinforcement Learning and Computational Intelligence. In this paper, we make a comparison of different optimization algorithms and a first investigation of how they can perform in this kind of scenario. Specifically, this comparison included the Deep Q-Network, Proximal Policy Optimization, Genetic Algorithms, Particle Swarm Optimization, Quantum-inspired Particle Swarm Optimization, Multi-Swarm Particle Optimization, and the Grey-Wolf Optimizer. We demonstrate how all approaches can solve the service management problem with similar performance—with a different sample efficiency—if a high number of samples can be evaluated for training and optimization. Finally, we show that, if the scenario conditions change, Deep-Reinforcement-Learning-based approaches can exploit the experience built during training to adapt service allocation according to the modified conditions.

Quantum-inspired particle swarm optimization for efficient IoT service placement in edge computing systems

With the advancement of the 5G networks, edge computing (EC) assisted Internet of Things (IoT) based applications demand real-time computation and high-volume data-intensive services. Due to the heterogeneity and limited resources of the edge nodes (ENs), and dynamic resource demand of the IoT applications, it is challenging to place the IoT services into the available ENs by ensuring performance measurements on quality of services (QoS). In this paper, a novel quantum-inspired particle swarm optimization-based service placement (QPSO-SP) algorithm is proposed for EC environment. The QPSO-SP is intended to achieve desired service placement while optimizing throughput, energy consumption, delay, and computation load of the system. Quantum particle (QP) is designed to represent a complete solution for IoT service placement in an EC environment. Decoding of the QP is done by using a novel double-hashing technique. The fitness function uses throughput, delay, energy consumption, and load balancing parameters. Extensive simulation is performed and comparison is done with the standard existing algorithms. The parametric study, Taguchi method is conducted. The statistical analysis, ANOVA, followed by Friedman test is also done. The simulation results indicate that the proposed QPSO-SP outperforms existing works in terms of energy consumption, delay, throughput, and load balancing.

Oppositional GOA Applied to Renewable Energy-Based Multi-Objective Economic Emission Dispatch

The renewable economic emission transmit is a significant and new assignment in the modern power system. This article develops oppositional grasshopper optimization algorithm (OGOA) which depends on the social dealings of the grasshopper in nature, to solve renewable energy based economic emission dispatch (EED) considering uncertainty in wind power availability and a carbon tax on emission from the thermal unit. To speed up the convergence speed and advance the simulation results, opposition based learning (OBL) is integrated with the fundamental GOA in OGOA algorithm. To show the nonlinearity of wind power availability the Weibull distribution is used. A standard system, containing of two wind farms and six thermal units is used for testing the dispatch model for three different loads. The statistical outcomes of the applied OGOA technique are compared with basic GOA and quantum-inspired particle swarm optimization (QPSO) optimization. It is observed OGOA is more skillful than basic GOA technique for significantly reducing the computation time and developing hopeful outcomes.

A Surrogate Assisted Quantum-Behaved Algorithm for Well Placement Optimization

The oil and gas industry faces difficulties in optimizing well placement problems. These problems are multimodal, non-convex, and discontinuous in nature. Various traditional and non-traditional optimization algorithms have been developed to resolve these difficulties. Nevertheless, these techniques remain trapped in local optima and provide inconsistent performance for different reservoirs. This study thereby presents a Surrogate Assisted Quantum-behaved Algorithm to obtain a better solution for the well placement optimization problem. The proposed approach utilizes different metaheuristic optimization techniques such as the Quantum-inspired Particle Swarm Optimization and the Quantum-behaved Bat Algorithm in different implementation phases. Two complex reservoirs are used to investigate the performance of the proposed approach. A comparative study is carried out to verify the performance of the proposed approach. The result indicates that the proposed approach provides a better net present value for both complex reservoirs. Furthermore, it solves the problem of inconsistency exhibited in other methods for well placement optimization.

Many-Objective Quantum-Inspired Particle Swarm Optimization Algorithm for Placement of Virtual Machines in Smart Computing Cloud.

Particle swarm optimization algorithm (PSO) is an effective metaheuristic that can determine Pareto-optimal solutions. We propose an extended PSO by introducing quantum gates in order to ensure the diversity of particle populations that are looking for efficient alternatives. The quality of solutions was verified in the issue of assignment of resources in the computing cloud to improve the live migration of virtual machines. We consider the multi-criteria optimization problem of deep learning-based models embedded into virtual machines. Computing clouds with deep learning agents can support several areas of education, smart city or economy. Because deep learning agents require lots of computer resources, seven criteria are studied such as electric power of hosts, reliability of cloud, CPU workload of the bottleneck host, communication capacity of the critical node, a free RAM capacity of the most loaded memory, a free disc memory capacity of the most busy storage, and overall computer costs. Quantum gates modify an accepted position for the current location of a particle. To verify the above concept, various simulations have been carried out on the laboratory cloud based on the OpenStack platform. Numerical experiments have confirmed that multi-objective quantum-inspired particle swarm optimization algorithm provides better solutions than the other metaheuristics.

A comparative study of nonlinear regression and autoregressive techniques in hybrid with particle swarm optimization for time-series forecasting

Usually, real-world time-series forecasting problems are dynamic. If such time-series are characterized by mere concept shifts, a passive approach to learning become ideal to continuously adapt the model parameters whenever new data patterns arrive to cope with uncertainty in the presence of change. This work hybridizes a quantum-inspired particle swarm optimization designed for dynamic environments, to cope with concept shifts, with either a least-squares approximation technique or nonlinear autoregressive model to forecast time-series. Also, this work evaluates experimentally and performs a comparative study on the performance of the proposed models. The obtained results show that the nonlinear autoregressive-based model outperformed the least-squares approximation-based model and the separate models that were implemented in the hybridization and also, several state-of-the-art models for the given 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.

An Integrated Model of Train Re-Scheduling and Control for High-Speed Railway

The goal of train re-scheduling is redesigning the time when trains arrive at and depart from stations of a railway section, and train control problem refers to determining the operating mode for a train in a railway section. It is quite necessary to study the two problems together, and they can be viewed as a theory base for self-driving study. We build a novel model to deal with train re-scheduling and train control problem synthetically. The approach is divided into two stages. The first stage is train re-scheduling, determining the arrival and departure time for trains. Depending on the arrival and departure time, the train running time can be calculated and it is set to be the constraint of the train control model. The destination of the second stage model is to save tracking energy in train operation process, determining the traction plan in each segment of a section between two stations. We also design a quantum-inspired particle swarm optimization algorithm to solve the integrated model. A computation case is presented to prove the availability of the approach. It can generate the re-scheduled timetable and train control plan synthetically with the approach presented in this paper. The main contribution of this paper is to propose a novel approach to solve train re-scheduling problem and train control problem synthetically. It can also provide supporting information for both the dispatchers and the train drivers to improve the on schedule rate and reduce the energy consumption. Furthermore, it may provide some valuable reference for the realization of automatic train driving.

Handling multimodal multi-objective problems through self-organizing quantum-inspired particle swarm optimization

Multimodal multi-objective optimization problems (MMOPs) involve locating equivalent Pareto optimal solutions in decision space with the same objective values. The key to handling MMOPs is finding all equivalent Pareto optimal solutions and maintaining a promising balance between the convergence and diversity of solutions in both decision space and objective space. To tackle this issue, a self-organizing quantum-inspired particle swarm optimization algorithm (MMO_SO_QPSO) is proposed in this paper for handling MMOPs. In the proposed MMO_SO_QPSO, a self-organizing map is used to find the best neighbor leader of particles. With the aid of neighbor leader particles, a special zone searching method is adopted to update the position of particles and locate equivalent Pareto optimal solutions in decision space. To maintain diversity and convergence of Pareto optimal solutions, a special archive mechanism that relies on the maximum-minimum distance among solutions is introduced into MMO_SO_QPSO. And some outstanding Pareto optimal solutions are maintained in the special archive. In addition, a new performance indicator is developed to estimate properly the similarity between obtained Pareto optimal solutions and true Pareto optimal solutions. The performance of the proposed MMO_SO_QPSO is compared with six state-of-the-art multimodal multi-objective evolutionary algorithms on two well-known benchmark problems. Experimental results demonstrate the superior performance of MMO_SO_QPSO for solving MMOPs. The effectiveness of several strategies is also discussed.

A quantum-inspired particle swarm optimization for sizing optimization of truss structures

Particle swarm optimization (PSO) algorithm has some advantages such as simple principle, less model parameters. However, it has some flaws for dealing with sizing optimization of truss structures, such as low accuracy, slow astringency and poor population diversity in the later evolution stage. In order to overcome these shortcomings and deal with optimization problem of truss structures effectively, a new quantum-inspired particle swarm optimization (QPSO) algorithm with quantum angle encoding and a perturbation operator of the population is proposed in this paper. Then, QPSO is used for optimization of truss structures by means of penalty function method. In the end, two typical numerical tests for truss design are performed to calibrate and verified the proposed algorithm. The results of the QPSO compared with PSO and other optimization algorithms show that the proposed QPSO has a better performance in terms of both the optimum solutions and the convergence capability than PSO and is a powerful optimization technique for solving optimization problem of truss structures.

Quantum inspired Particle Swarm Optimization with guided exploration for function optimization

The complex function optimization problems, which have properties like multimodality, high dimensionality, non-differentiability, non-linear parameter interactions, are challenging and hard to solve. A number of meta-heuristic algorithms are proposed to find near optimal solution to these complex problems. However, most of them suffer from poor exploration and get caught in local optima. In order to overcome this problem, we present an enhanced Quantum behaved Particle Swarm Optimization (e-QPSO) algorithm, which improves the exploration and the exploitation properties of the original QPSO for function optimization. We introduce the adaptive balance among the personal best and the global best positions using the parameter alpha and achieve the balance between the diversification and the intensification using the parameter gamma. Further, we re-initialize a percentage of the worst performing population to help escape the particle from the local optima. These three modifications in the e-QPSO play crucial role to enhance the performance of the original QPSO algorithm. The e-QPSO is validated on 59 well-known challenging benchmark problems including 5 engineering problems. The results of e-QPSO outperform those of existing twelve QPSO variants, two adaptive variants of PSO, as well as nine well-known evolutionary algorithms. Statistical tests also demonstrate the statistically significant performance of the e-QPSO compared to the other meta-heuristic methods.

A Hybrid Quantum Inspired Particle Swarm Optimization and Least Square Framework for Real-time Harmonic Estimation

A Hybrid Quantum Inspired Particle Swarm Optimization and Least Square Framework for Real-time Harmonic Estimation

A Novel Quantum Inspired Particle Swarm Optimization Algorithm for Electromagnetic Applications

Quantum inspired particle swarm optimization (QPSO) stimulated by perceptions from particle swarm optimization and quantum mechanics is a stochastic optimization method. Although, it has shown good performance in finding the optimal solution to many electromagnetic problems. However, sometimes it falls to local optima when dealing with hard optimization problems. Thus, to preserve a good balance between local and global searches to avoid premature convergence in quantum particle swarm optimization, this paper proposed three enhancements to the original QPSO method, the proposed method is called modified quantum particle swarm optimization (MQPSO) algorithm. Firstly, a novel selection technique is introduced that will choose the best particle among the population within the search domain to achieve a high-performance exploration. Secondly, a new mutation method is used to preserve the easiness of available QPSOs. Also, a dynamic parameter strategy is proposed for further facilitating the algorithm and tradeoff between exploration and exploitation searches. The experimental results obtained by solving standard benchmark functions and an electromagnetic design problem which is the superconducting magnetic energy storage (SMES) system available in both three parameters and eight parameters problems are reported to showcase the usefulness of the proposed approach.

Novel quantum inspired approaches for automatic clustering of gray level images using Particle Swarm Optimization, Spider Monkey Optimization and Ageist Spider Monkey Optimization algorithms

This paper is intended to identify the optimal number of clusters automatically from an image dataset using some quantum behaved nature inspired meta-heuristic algorithms. Due to the lack of sufficient information, it is difficult to identify the appropriate number of clusters from a dataset, which has enthused the researchers to solve the problem of automatic clustering and to open up a new era of cluster analysis with the help of several natures inspired meta-heuristic algorithms. In this paper, three quantum inspired meta-heuristic techniques, viz., Quantum Inspired Particle Swarm Optimization (QIPSO), Quantum Inspired Spider Monkey Optimization (QISMO) and Quantum Inspired Ageist Spider Monkey Optimization (QIASMO), have been proposed. A comparison has been outlined between the quantum inspired algorithms with their corresponding classical counterparts. The efficiency of the quantum inspired algorithms has been established over their corresponding classical counterparts with regards to fitness, mean, standard deviation, standard errors of fitness, convergence curves (for benchmarked mathematical functions) and computational time. Finally, the results of two statistical superiority tests, viz., t- test and Friedman test have been provided to prove the superiority of the proposed methods. The superiority of the proposed methods has been established on five publicly available real life image datasets, five Berkeley image datasets of different dimensions and four benchmark mathematical functions both visually and quantitatively.

Economic Load Dispatch with Valve Point Effect Using Quantum Inspired Particle Swarm Optimization

Economic Load Dispatch with Valve Point Effect Using Quantum Inspired Particle Swarm Optimization

Energy efficient cooperative coalition selection in cluster-based capillary networks for CMIMO IoT systems

The Cooperative Multiple-input-multiple-output (CMIMO) scheme has been suggested to extend the lifetime of cluster heads (CHs) in cluster-based capillary networks in Internet of Things (IoT) systems. However, the CMIMO scheme introduces extra energy overhead to cooperative devices and further reduces the lifetime of these devices. In this paper, we first articulate the problem of cooperative coalition’s selection for CMIMO scheme to extend the average battery capacity among the whole network, and then propose to apply the quantum-inspired particle swarm optimization (QPSO) to select the optimum cooperative coalitions of each hop in the routing path. Simulation results proved that the proposed QPSO-based cooperative coalition’s selection scheme could select the optimum cooperative sender and receiver devices in every hop dynamically and outperform the virtual MIMO scheme with a fixed number of cooperative devices.

Renewable Energy Based Economic Emission Load Dispatch Using Grasshopper Optimization Algorithm

This article presents an integrated approach towards the economical operation of a hybrid system which consists of conventional thermal generators and renewable energy sources like windmills using a grasshopper optimization algorithm (GOA). This is based on the social interaction nature of the grasshopper, considering a carbon tax on the emissions from the thermal unit and uncertainty in wind power availability. The Weibull distribution is used for nonlinearity of wind power availability. A standard system, containing six thermal units and two wind farms, is used for testing the dispatch model of three different loads. The GOA results are compared with those obtained using a recently developed quantum-inspired particle swarm optimization (QPSO) optimization technique available in the literature. The simulation results demonstrate the efficacy and ability of GOA over the QPSO algorithm in terms of convergence rate and minimum fitness value. Performance analysis under wind power integration and emission minimization further confirms the supremacy of the GOA algorithm.