Quantum-inspired differential evolution (QDE) is an evolutionary algorithm, which can effectively solve complex optimization problems. However, sometimes, it easily leads to premature convergence and low search ability and falls to local optima. To overcome these problems, based on the MSIQDE (improved QDE with multistrategies) algorithm, an enhanced MSIQDE algorithm based on mixing multiple strategies, namely, EMMSIQDE is proposed in this article. In the EMMSIQDE, a new differential mutation strategy of a difference vector is proposed to enhance the search ability and descent ability. Then, a new multipopulation mutation evolution mechanism is designed to ensure the relative independence of each subpopulation and the population diversity. The feasible solution space transformation strategy is used to achieve the optimal solution by mapping the quantum chromosome from a unit space to solution space. Finally, some multidimensional unimodal and multimodal functions are selected to demonstrate the optimization performance of EMMSIQDE. The results demonstrate that the EMMSIQDE is significantly better than the DE, QDE, QGA, and MSIQDE, and has better optimization ability, scalability, efficiency, and stability. 

An Enhanced MSIQDE Algorithm With Novel Multiple Strategies for Global Optimization Problems

In order to overcome the low solution efficiency, insufficient diversity in the later search stage, slow convergence speed and a high search stagnation possibility of differential evolution(DE) algorithm, the quantum computing characteristics of quantum evolutionary algorithm(QEA) and the divide-and-conquer idea of cooperative coevolution evolutionary algorithm(CCEA) are combined to propose an improved differential evolution(HMCFQDE) in this paper. In the proposed HMCFQDE, a new hybrid mutation strategy based on the advantages of local neighborhood mutation and SaNSDE is designed. In the early stage of the search, the local neighborhood mutation strategy with high search efficiency is used to speed up the algorithm convergence. In the later stage of the search, the SaNSDE algorithm is used to adjust the search direction in order to avoid the search stagnation. The QEA is combined with the DE to make use of the quantum chromosome encoding to enhance the population diversity, the quantum rotation to speed up the convergence speed. The CC framework is used to divide the large-scale and high-dimensional complex optimization problem into several low-dimensional optimization sub-problems, and these sub-populations are solved by independent searching among sub-populations in order to improve the solution efficiency. By comparing with other 6 algorithms in solving 6 test functions from CEC’08 under the dimensions of 100, 500 and 1000, it is proved that the proposed HMCFQDE has higher convergence accuracy and stronger stability. In particular, it has a strong ability to optimize high-dimensional complex functions. Therefore, it provides a new method for solving large-scale optimization problem.

Quantum differential evolution with cooperative coevolution framework and hybrid mutation strategy for large scale optimization

It is feasible and safe to use unmanned aerial vehicle (UAV) as the data collection platform of the Internet of things (IoT). In order to save the energy loss of the platform and make the UAV perform the collection work effectively, it is necessary to optimize the deployment of UAV. The objective problem is to minimize the sum of the lost energy of UAV and the loss of data transmission of Internet of things devices. The key to solving the problem is to calculate the location of the docking points and the number of docking points when the UAV is working to collect data. This paper proposes a coding scheme based on swarm intelligence optimization, which encapsulates the docking position of UAV into a dimension, so the number of docking points to be calculated is the dimension number of optimization objective. This problem is considered as a dynamic dimension optimization problem. Each individual in swarm intelligence algorithm is a solution. When adjusting the dimension, the best individual is added or deleted to achieve dynamic search in the evolutionary process. Collaborative search among multiple individuals can improve the local optimal limit of search to a certain extent. Finally, the validity of the swarm intelligence-based coding approach is verified by simulation under seven IoT device distribution scenarios. The swarm intelligence algorithms we used are flower pollination algorithm (FPA), salp swarm algorithm (SSA), sine cosine algorithm (SCA). FPA and SCA perform most efficiently in three and four scenarios among the seven IoT device scenarios, respectively.

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Swarm Intelligence Application to UAV Aided IoT Data Acquisition Deployment Optimization

An optimized design with real-time and multiple realistic constraints in complex engineering systems is a crucial challenge for designers. In the non-uniform Internet of Things (IoT) node deployments, the approximation accuracy is directly affected by the parameters like node density and coverage. We propose a novel enhanced differential crossover quantum particle swarm optimization algorithm for solving nonlinear numerical problems. The algorithm is based on hybrid optimization using quantum PSO. Differential evolution operator is used to circumvent group moves in small ranges and falling into the local optima and improves global searchability. The cross operator is employed to promote information interchange among individuals in a group, and exceptional genes can be continued moderately, accompanying the evolutionary process’s continuance and adding proactive and reactive features. The proposed algorithm’s performance is verified as well as compared with the other algorithms through 30 classic benchmark functions in IEEE CEC2017, with a basic PSO algorithm and improved versions. The results show the smaller values of fitness function and computational efficiency for the benchmark functions of IEEE CEC2019. The proposed algorithm outperforms the existing optimization algorithms and different PSO versions, and has a high precision and faster convergence speed. The average location error is substantially reduced for the smart parking IoT application.

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Enhanced Differential Crossover and Quantum Particle Swarm Optimization for IoT Applications

In this article, a method for design automation high-efficiency power amplifiers (PAs) based on an optimization-oriented is proposed. The advantages of using particle swarm optimization to design PAs in electronic design automatic tools are analyzed, and then, simulated annealing is combined to form simulated annealing particle swarm optimization (SA-PSO). In this article, the perfectly inelastic collision used in conventional PSO is replaced by the perfectly elastic collision to improve the searching ability through reducing kinetic energy consumption. A stepped fitness function is defined to avoid the performance pit at a single-frequency point, and the concept “missing and returning” is introduced to solve the nonconvergence problem in simulation. Based on the improved SA-PSO algorithm, a broadband 10 W and a triband 6-W PA are designed. The broadband 10-W PA achieves an average drain efficiency (DE) of 62.5% and an output power of 41.2 dBm in the 0.8–3-GHz frequency band, respectively; the triband 6-W PA achieves an average of 57.4%/47.9%/47.3% DE and 37.7-/38.3-/37.4 dBm output power in 3.35–3.55-/5.75–5.95-/7.65–7.95-GHz frequency bands, respectively, while the gains of most stopbands reach less than 0 dB. With digital predistortion technology, the adjacent channel power ratio (ACPR) can reach less than −45 dBc.

Simulated Annealing Particle Swarm Optimization for High-Efficiency Power Amplifier Design

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.

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A Novel Quantum Inspired Particle Swarm Optimization Algorithm for Electromagnetic Applications

Predicting cardiac disease is considered one of the most challenging tasks in the medical field. It takes a lot of time and effort to figure out what’s causing this, especially for doctors and other medical experts. In this paper, various Machine Learning algorithms such as LR, KNN, SVM, and GBC, together with the GridSearchCV, predict cardiac disease. The system uses a 5-fold cross-validation technique for verification. A comparative study is given for these four methodologies. The Datasets for both Cleveland, Hungary, Switzerland, and Long Beach V and UCI Kaggle are used to analyze the models’ performance. It is found in the analysis that the Extreme Gradient Boosting Classifier with GridSearchCV gives the highest and nearly comparable testing and training accuracies as 100% and 99.03% for both the datasets (Hungary, Switzerland & Long Beach V and UCI Kaggle). Moreover, it is found in the analysis that XGBoost Classifier without GridSearchCV gives the highest and nearly comparable testing and training accuracies as 98.05% and 100% for both the datasets (Hungary, Switzerland & Long Beach V and UCI Kaggle). Furthermore, the analytical results of the proposed technique are compared with previous heart disease prediction studies. It is evident that amongst the proposed approach, the Extreme Gradient Boosting Classifier with GridSearchCV is producing the best hyperparameter for testing accuracy. The primary aim of this paper is to develop a unique model-creation technique for solving real-world problems.

https://ieeexplore.ieee.org/ielx7/6287639/6514899/09751602.pdf

Efficient Medical Diagnosis of Human Heart Diseases using Machine Learning Techniques with and without GridSearchCV

Predicting heart disease is regarded as one of the most difficult challenges in the health-care profession. To predict cardiac disease, researchers employed a variety of algorithms including LDA, RF, GBC, DT, SVM, and KNN, as well as the feature selection algorithm sequential feature selection. For verification, the system employs the K-fold cross-validation approach. These six strategies were used to conduct the comparative study. The Dataset for Cleveland, Hungray, Switzerland, and Long Beach V, as well as the Dataset Heart Statlog Cleveland Hungary, were used to assess the models performance. For both Hungary, Switzerland & Long Beach V and Heart Statlog Cleveland Hungary Dataset, Random Forest Classifier sfs and Decision Tree Classifier sfs produced the highest and almost identical accuracy values (100%, 99.40% and 100%, 99.76% respectively). The findings were compared to previous research that focused on cardiac prediction. In the future, we hope to extend the model even further so that it may be used with various feature selection techniques; another possibility is to use a random forest classifier. The major goal of this study is to improve on previous work by developing a new and unique technique for creating the model, as well as to make the model relevant and easy to use in real-world situations.

https://ieeexplore.ieee.org/ielx7/6287639/9668973/09718089.pdf

Comparative Study of Optimum Medical Diagnosis of Human Heart Disease Using Machine Learning Technique With and Without Sequential Feature Selection

A significant obstacle to creating efficient machine health monitoring systems is estimating performance degradation in dynamic systems, like aero plane engines. In exceedingly complex systems with many components, states, and parameters, conventional model-based and data-driven methods fall short of producing satisfactory results. While traditional methods had several drawbacks, deep learning has emerged as a viable computational tool for dynamic system prediction. In order to track system deterioration and estimate the EGT, a novel technique based on the Long Short-Term Memory (LSTM) network, (an architecture created to find the hidden patterns hidden in time series data) is provided in this research. The health monitoring information of aircraft turbofan engines is used to assess the effectiveness of the proposed strategy. As a result of this network’s ability to recognize the input data as a real-time series, the output in the following step can be predicted. Results of the suggested study show a significant ability to anticipate the output in the following time step. Additionally, the proposed model has a shorter learning curve and is more accurate.

https://ieeexplore.ieee.org/ielx7/6287639/6514899/10012388.pdf

An Investigation of Exhaust Gas Temperature of Aircraft Engine Using LSTM

Machine-type communication devices have become a vital part of the autonomous industrial internet of things and industry 4.0. These autonomous resource-constrained devices share sensitive data, and are primarily acquired for automation and to operate consistently in remote environments under severe conditions. The requirements to secure the sensitive data shared between these devices consist of a resilient encryption technique with affordable operational costs. Consequently, devices, data, and networks are made secure by adopting a lightweight cryptosystem that should achieve robust security with sufficient computational and communication costs and counter modern security threats. This paper offers in-depth studies on different types and techniques of hardware and software-based lightweight cryptographies for machine-type communication devices in machine-to-machine communication networks.

Shafi Ullah

Papers

A Novel Quantum Inspired Particle Swarm Optimization Algorithm for Electromagnetic Applications

Efficient Medical Diagnosis of Human Heart Diseases using Machine Learning Techniques with and without GridSearchCV

Comparative Study of Optimum Medical Diagnosis of Human Heart Disease Using Machine Learning Technique With and Without Sequential Feature Selection

An Investigation of Exhaust Gas Temperature of Aircraft Engine Using LSTM

Types of Lightweight Cryptographies in Current Developments for Resource Constrained Machine Type Communication Devices: Challenges and Opportunities