Individual Disturbance and Attraction Repulsion Strategy Enhanced Seagull Optimization for Engineering Design

The seagull optimization algorithm (SOA) is a novel swarm intelligence algorithm proposed in recent years. The algorithm has some defects in the search process. To overcome the problem of poor convergence accuracy and easy to fall into local optimality of seagull optimization algorithm, this paper proposed a new variant SOA based on individual disturbance (ID) and attraction-repulsion (AR) strategy, called IDARSOA, which employed ID to enhance the ability to jump out of local optimum and adopted AR to increase the diversity of population and make the exploration of solution space more efficient. The effectiveness of the IDARSOA has been verified using representative comprehensive benchmark functions and six practical engineering optimization problems. The experimental results show that the proposed IDARSOA has the advantages of better convergence accuracy and a strong optimization ability than the original SOA.

Differential Evolution and Multiclass Support Vector Machine for Alzheimer’s Classification

Early diagnosis of Alzheimer’s helps a doctor to decide the treatment for the patient based on the stages. The existing methods involve applying the deep learning methods for Alzheimer’s classification and have the limitations of overfitting problems. Some researchers were involved in applying the feature selection based on the optimization method, having limitations of easily trapping into local optima and poor convergence. In this research, Differential Evolution-Multiclass Support Vector Machine (DE-MSVM) is proposed to increase the performance of Alzheimer’s classification. The image normalization method is applied to enhance the quality of the image and represent the features effectively. The AlexNet model is applied to the normalized images to extract the features and also applied for feature selection. The Differential Evolution method applies Pareto Optimal Front for nondominated feature selection. This helps to select the feature that represents the characteristics of the input images. The selected features are applied in the MSVM method to represent in high dimension and classify Alzheimer’s. The DE-MSVM method has accuracy of 98.13% in the axial slice, and the existing whale optimization with MSVM has 95.23% accuracy.

Finder-MCTS: A Cognitive Spectrum Allocation Based on Traveling State Priority and Scenario Simulation in IoV

With the increasing number of intelligent connected vehicles, the problem of scarcity of communication resources has become increasingly obvious. It is a practical issue with important significance to explore a real-time and reliable dynamic spectrum allocation scheme for the vehicle users, while improving the utilization of the available spectrum. However, previous studies have problems such as local optimum, complex parameter setting, learning speed, and poor convergence. Thus, in this paper, we propose a cognitive spectrum allocation method based on traveling state priority and scenario simulation in IoV, named Finder-MCTS. The proposed method integrates offline learning with online search. This method mainly consists of two stages. Initially, Finder-MCTS gives the allocation priority of different vehicle users based on the vehicle’s local driving status and global communication status. Furthermore, Finder-MCTS can search for the approximate optimal allocation solutions quickly online according to the priority and the scenario simulation, while with the offline deep neural network-based environmental state predictor. In the experiment, we use SUMO to simulate the real traffic flows. Numerical results show that our proposed Finder-MCTS has 36.47%, 18.24%, and 9.00% improvement on average than other popular methods in convergence time, link capacity, and channel utilization, respectively. In addition, we verified the effectiveness and advantages of Finder-MCTS compared with two MCTS algorithms’ variations.

Cuts for two-body decays at colliders

Fixed-order perturbative calculations of fiducial cross sections for two-body decay processes at colliders show disturbing sensitivity to unphysically low momentum scales and, in the case of H → γγ in gluon fusion, poor convergence. Such problems have their origins in an interplay between the behaviour of standard experimental cuts at small transverse momenta (pt) and logarithmic perturbative contributions. We illustrate how this interplay leads to a factorially divergent structure in the perturbative series that sets in already from the first orders. We propose simple modifications of fiducial cuts to eliminate their key incriminating characteristic, a linear dependence of the acceptance on the Higgs or Z-boson pt, replacing it with quadratic dependence. This brings major improvements in the behaviour of the perturbative expansion. More elaborate cuts can achieve an acceptance that is independent of the Higgs pt at low pt, with a variety of consequent advantages.

A Novel Optimized Deep Learner with Lightweighted Encryption-Based Speaker Verification

In the modern digitalized world, Speaker verification (SV) system is essential for authorizing the client’s credentials. To design an effective SV system, MGWOVSW-CAES-GMM system has been proposed. In this system, the Modified Grey Wolf Optimization (MGWO) technique was employed to optimize the variable sliding window size, FMPM features and training variables. The optimized features were watermarked and encrypted using a Chaotic-based Advanced Encryption Standard (CAES). Once the encryption process was completed, the encrypted features were forwarded to the recipient who executes the decryption and de-watermarking processes. At last, the decrypted features were classified using Gaussian Mixture Model (GMM) classifier. Conversely, MGWO has poor convergence rate and ineffective searching results. Hence, this article proposes an EEHOVSW-CAES-GMM system in which Enhanced Elephant Herding Optimization (EEHO) algorithm is applied instead of MGWO. On the contrary, the computational complexity of GMM classifier is high and its efficiency is less while increasing the number of features. For this reason, a Deep Neural Network (DNN) classifier is employed instead of GMM for recognizing the decrypted features and authorize the speaker’s identity. Besides, the parameters utilized in DNN topology are optimized using two different systems such as MGWOVSW-CAES-DNN and EEHOVSW-CAES-DNN for reducing the computational complexity and increasing the classification accuracy effectively when using more number of features. By using these classifiers, the speaker’s identity is verified and the attacks during the transmission are prevented with the highest security level.

COD Optimization Prediction Model Based on CAWOA-ELM in Water Ecological Environment

The change of water quality can reflect the important indicators of ecological environment measurement. Sewage discharge is an important factor causing environmental pollution. Establishing an effective water ecological prediction model can detect changes in the ecological environment system quickly and effectively. In order to detect high error rate and poor convergence of the water ecological chemical oxygen demand (COD) prediction model, combining the limit learning machine (ELM) model and whale optimization algorithm, CAWOA is improved by the sin chaos search strategy, while the ELM optimizes the parameters of the algorithm to improve convergence speed, thus improving the generalization performance of the ELM. In the CAWOA, the global optimization results of the WOA are promoted by introducing a sin chaotic search strategy and adaptive inertia weights. On this basis, the COD prediction model of CAWOA-ELM is established and compared with similar algorithms by using the optimized ELM to predict the water ecological COD in a region. Finally, from the experimental results of the CAWOA-ELM algorithm, it has excellent prediction effect and practical application value.

On the spherical expansion for calculating the sound radiated by a baffled circular piston

An efficient and accurate method for calculating the sound radiated by a baffled circular rigid piston is using spherical harmonics, and the solution is a series containing the integral of spherical Bessel functions. The integral is usually calculated with the generalized hypergeometric functions in existing literatures, which shows poor convergence at middle and high frequencies due to the overflow and the loss of significant figures. A rigorous and closed form solution of the integral is derived in this paper based on the recurrence method, which is accurate in the whole frequency range and thousands of times faster than the existing methods. It is shown that the proposed method can be extended for the calculation of the sound radiated by a baffled piston and an unbaffled resilient disk with axisymmetric velocity and pressure profiles, respectively, and some baffled rotating sources where the velocity profile is asymmetric.

MS renormalization of S-wave quarkonium wavefunctions at the origin

We compute S-wave quarkonium wavefunctions at the origin in the $$ \overline{\mathrm{MS}} $$ MS ¯ scheme based on nonrelativistic effective field theories. We include the effects of nonperturbative long-distance behaviors of the potentials, while we determine the short-distance behaviors of the potentials in perturbative QCD. We obtain $$ \overline{\mathrm{MS}} $$ MS ¯ -renormalized quarkonium wavefunctions at the origin that have the correct scale dependences that are expected from perturbative QCD, so that the scale dependences cancel in physical quantities. Based on the calculation of the wavefunctions at the origin, we make model-independent predictions of decay constants and electromagnetic decay rates of S-wave charmonia and bottomonia, and compare them with measurements. We find that the poor convergence of perturbative QCD corrections are substantially improved when we include corrections to the wavefunctions at the origin in the calculation of decay constants and decay rates.

On the learnability of quantum neural networks

Quantum neural network (QNN), or equivalently, the variational quantum circuits with a gradient-based classical optimizer, has been broadly applied to many experimental proposals for noisy intermediate scale quantum (NISQ) devices. However, the learning capability of QNN remains largely unknown due to the non-convex optimization landscape, the measurement error, and the unavoidable gate noise introduced by NISQ machines. In this study, we theoretically explore the learnability of QNN from the perspective of the trainability and generalization. Particularly, we derive the convergence performance of QNN under the NISQ setting, and identify classes of computationally hard concepts that can be efficiently learned by QNN. Our results demonstrate that large gate noise, few quantum measurements, and deep circuit depth will lead to poor convergence rates of QNN towards the empirical risk minimization. Moreover, we prove that any concept class, which is efficiently learnable by a restricted quantum statistical query (QSQ) learning model, can also be efficiently learned by QNN. Since the restricted QSQ learning model can tackle certain problems such as parity learning with a runtime speedup, our result suggests that QNN established on NISQ devices will retain the quantum advantage. Our work provides the theoretical guidance for developing advanced QNNs and opens up avenues for exploring quantum advantages using NISQ devices.

A Biological Immune Mechanism-Based Quantum PSO Algorithm and Its Application in Back Analysis for Seepage Parameters

Back analysis for seepage parameters is a classic issue in hydraulic engineering seepage calculations. Considering the characteristics of inversion problems, including high dimensionality, numerous local optimal values, poor convergence performance, and excessive calculation time, a biological immune mechanism-based quantum particle swarm optimization (IQPSO) algorithm was proposed to solve the inversion problem. By introducing a concentration regulation strategy to improve the population diversity and a vaccination strategy to accelerate the convergence rate, the modified algorithm overcame the shortcomings of traditional PSO which can easily fall into a local optimum. Furthermore, a simple multicore parallel computation strategy was applied to reduce computation time. The effectiveness and practicability of IQPSO were evaluated by numerical experiments. In this paper, taking one concrete face rock-fill dam (CFRD) as a case, a back analysis for seepage parameters was accomplished by utilizing the proposed optimization algorithm and the steady seepage field of the dam was analysed by the finite element method (FEM). Compared with immune PSO and quantum PSO, the proposed algorithm had better global search ability, convergence performance, and calculation rate. The optimized back analysis could obtain the permeability coefficient of CFRD with high accuracy.