Improved Quantum-behaved Particle Swarm Optimization Algorithm Research Articles

Multi-objective Reactive Power Optimization of a Distribution Network based on Improved Quantum-behaved Particle Swarm Optimization

Background: Reactive power optimization (RPO) is crucial for distribution networks in the context of large-scale renewable distributed generation (RDG) access. Objective: To address the problems caused by the connection of RDG, an RPO model and an improved quantum-behaved particle swarm optimization (IQPSO) algorithm are proposed. Method: In this study, a dynamic S-type function is proposed as the objective function of the minimum active power loss, whereas an exponential function is proposed as the objective function of the minimum voltage deviation to establish an RPO objective function. The operating cost of distribution is considered as the third objective function. To address the RPO problem, a QPSO algorithm based on the ε-greedy strategy is proposed in this paper. ModifiedIEEE33 bus and IEEE69 bus systems were used to evaluate the proposed RPO method in simulations. Results: The simulation results reveal that the IQPSO algorithm obtains a better solution, and the proposed RPO model can considerably reduce active power loss, node voltage deviation, and distribution network operating costs. Conclusion: The RPO model and IQPSO algorithm proposed in this study provide a highperformance method to analyze and optimize reactive power management in distribution network.

Multi-performance Optimization of the Rotary Turning Operation for Environmental and Quality Indicators

In this investigation, two environmental metrics (the comprehensive energy used (TU) and turning noise (TN)) and a quality metric (surface roughness (SR)) of the rotary turning process for the Ti6Al4V were optimized and reduced using the optimal factors (the inclined angle-i, depth of cut-d, feed-f, and turning speed-V). The TU model was proposed comprising the embodied energy of the insert and lubricant. The method based on the removal effects of criteria (MEREC), an improved quantum-behaved particle swarm optimization algorithm (IQPSO), and TOPSIS were applied to select weight values and the best optimal solution. The machining cost (MC) was proposed in terms of process parameters. The outcomes presented that the optimal values of the i, d, f, and V were 35 deg., 0.30 mm, 0.40 mm/rev., and 190 m/min, respectively, while the TU, SR, TN, and MC were saved by 6.7 %, 22.3 %, 23.5 %, and 8.5 %, respectively. The turning responses were primarily affected by the feed rate and turning speed, respectively. The developed turning process could be employed for machining hard-to-cut alloys. The developed approach could be applied to deal with optimization problems for other machining operations.

A Word-Level Adversarial Attack Method Based on Sememes and an Improved Quantum-Behaved Particle Swarm Optimization.

The goal of textual adversarial attack methods is to replace some words in an input text in order to make the victim model misbehave. This article proposes an effective word-level adversarial attack method based on sememes and an improved quantum-behaved particle swarm optimization (QPSO) algorithm. The sememe-based substitute method, which uses the words sharing the same sememes as the substitutes of the original words, is first employed to form the reduced search space. Then, an improved QPSO algorithm, called historical information-guided QPSO with random drift local attractor (HIQPSO-RD), is proposed to search the reduced search space for adversarial examples. The HIQPSO-RD introduces historical information into the current mean best position of the QPSO, for the purpose of improving the convergence speed of the algorithm, by enhancing its exploration ability and preventing the premature convergence of the swarm. The proposed algorithm uses the random drift local attractor technique to make a good balance between its exploration and exploitation, so that the algorithm can find a better adversarial attack example with low grammaticality and perplexity (PPL). In addition, it employs a two-stage diversity control strategy to enhance the search performance of the algorithm. Experiments on three natural language processing (NLP) datasets, with three commonly used nature language processing models as victim models, show that our method achieves higher attack success rates but lower modification rates than the state-of-the-art adversarial attack methods. Moreover, the results of human evaluations show that adversarial examples generated by our method can better maintain the semantic similarity and grammatical correctness of the original input.

Lyapunov control of finite-dimensional quantum systems based on bi-objective quantum-behaved particle swarm optimization algorithm

This paper presents a Lyapunov control scheme to drive finite-dimensional closed and Markovian open quantum systems into any target pure state with as high fidelity and as little time as possible. The control law is established by a Lyapunov function with an unknown Hermitian operator, where the eigenvectors of the operator are constructed based on the condition that the LaSalle invariant set contains the desired target state and a set of optimal eigenvalues of the Hermitian operator is searched by the quantum-behaved particle swarm optimization (QPSO) algorithm. In particular, for open systems, when the denominator in the control law is quite small, we propose an improved QPSO algorithm with constraints to enhance the flexibility in implementation. Finally, numerical simulations are carried out on a five-level and a three-qubit closed quantum systems, and a five-level and a ten-level open quantum systems. Simulation results demonstrate that the proposed control scheme can achieve high-fidelity transfer to desired target states for high-dimensional closed and open quantum systems.

Multi-response optimization of the electrical discharge drilling operation: A trade-off relation between the quality and efficiency

In this work, the tool rotational speed (T), dielectric pressure (W), feed rate (F), and voltage (V) of the electrical discharge drilling (EDD) operation are optimized to decrease the extension of the drilled hole (EH) and improve the material removal rate (MRR). The Kriging models were utilized to present performance measures, while the Entropy approach and improved quantum-behaved particle swarm optimization algorithm (IQPSO) algorithm were employed to compute the weights and determine optimal factors. The findings presented that the optimal T, W, F, and V are 550 rpm, 36 kg/cm2, 30 mm/s, and 70 V, respectively. The EH is reduced by 33.0%, while the MRR is enhanced by 39.4%, as compared to the common values. The Kriging models provided acceptable accuracy for the prediction purpose. The V and F had significant impacts on the EH and MRR. The optimal data could be utilized to enhance the performance measures for the practical EDD process. The method comprising the Kriging, Entropy, and IQPSO was a prominent solution to deal with complicated optimization issues for the EDD operation.

Lithological classification by PCA-QPSO-LSSVM method with thermal infrared hyper-spectral data

To improve the accuracy and operation efficiency of lithological classification using a thermal infrared airborne hyper-spectral imager (TASI) data, an innovative combinatorial algorithm (PCA-QPSO-LSSVM) based on principal component analysis (PCA), quantum-behaved particle swarm optimization (QPSO), and least-squares support vector machines (LSSVM) is proposed. After pre-processing, the emissivity data was extracted from TASI data, and 27 types of lithological units were selected in comparison with the measured spectrum and Johns Hopkins University spectrum library. The PCA was used for statistical analysis, and the appropriate n principal components were selected instead of the original 32 bands of TASI emissivity data. Based on the LSSVM classification algorithm, the improved QPSO algorithm was used to optimize the regularization parameter γ and the kernel function σ2 in the classification process. Finally, test samples selected from TASI data were classified by PCA-QPSO-LSSVM. The results show that, compared with the traditional LSSVM algorithm, PCA-QPOS-LSSVM has a higher recognition accuracy and greater operation efficiency. The field verification of the study area was carried out in LiuYuan town, GanSu province, China, and the accuracy of the field verification was 74.36% for the classification result, which is more consistent with the actual lithological distribution compared with the LSSVM algorithm. However, there are some flaws in this paper, such as the low classification accuracy in Moyite and plauenite. In general, The PCA-QPSO-LSSVM algorithm is a practical algorithm for lithological classification of TASI data.

Intelligent Distribution of Fresh Agricultural Products in Smart City

With the construction of smart cities and the continuous improvement of people's living standards, residents’ demand for fresh agricultural products (FAPs) has increased dramatically. Therefore, reasonable arrangement for intelligent distribution of FAP in smart cities can effectively guarantee product quality, improve distribution efficiency, reduce distribution cost, and increase customer satisfaction. In actual distribution in smart city, road conditions are one of the important factors that affect the distribution. Therefore, according to the influence of road conditions on refrigerated vehicle's (RV's) speed, the RV's speed characteristic models are established. Meanwhile, according to the characteristics of FAP, the penalty cost function based on the time window is constructed. According to the idea of fuzzy logic, the customer satisfaction evaluation model is established. Then, in order to minimize the distribution costs and maximize customer satisfaction as the optimization goal of intelligent distribution in smart city, the mathematical model is built. For solving this model, an improved quantum-behaved particle swarm optimization algorithm (IQPSO) is proposed. Finally, the effectiveness of IQPSO is verified by simulation. The results show that IQPSO also achieves good results, and the model constructed can effectively balance the relationship between the distribution costs and customer satisfaction when distributing FAP in smart city.

An Adaptive Cultural Algorithm with Improved Quantum-behaved Particle Swarm Optimization for Sonar Image Detection

This paper proposes an adaptive cultural algorithm with improved quantum-behaved particle swarm optimization (ACA-IQPSO) to detect the underwater sonar image. In the population space, to improve searching ability of particles, iterative times and the fitness value of particles are regarded as factors to adaptively adjust the contraction-expansion coefficient of the quantum-behaved particle swarm optimization algorithm (QPSO). The improved quantum-behaved particle swarm optimization algorithm (IQPSO) can make particles adjust their behaviours according to their quality. In the belief space, a new update strategy is adopted to update cultural individuals according to the idea of the update strategy in shuffled frog leaping algorithm (SFLA). Moreover, to enhance the utilization of information in the population space and belief space, accept function and influence function are redesigned in the new communication protocol. The experimental results show that ACA-IQPSO can obtain good clustering centres according to the grey distribution information of underwater sonar images, and accurately complete underwater objects detection. Compared with other algorithms, the proposed ACA-IQPSO has good effectiveness, excellent adaptability, a powerful searching ability and high convergence efficiency. Meanwhile, the experimental results of the benchmark functions can further demonstrate that the proposed ACA-IQPSO has better searching ability, convergence efficiency and stability.

Improvement of Quantum-Behaved Particle Swarm Optimization Algorithm and Its Application to Ship Hull Waterlines Approximation Based on Non-Uniform Rational B-Spline

Nowadays, the optimization algorithm used in the ship design is a hot research area. The computation efficiency and stability of the algorithm depended on the number of design variables, the algorithm parameters, and operation process. In order to improve design efficiency and stability, this paper focuses on the waterlines approximation with as less control parameters as possible applying the improved quantum-behaved particle swarm optimization (QPSO) algorithm based on NURBS (Non-Uniform Rational B-Spline). The optimization model is constructed with the homogeneous coordinate component of the fitted waterline's control points as the design variables. The objective function is set to minimize the maximum relative difference between the widths of the approximated waterlines and the original ones corresponding to the same stations. The appropriate constraints are set according to the characteristics of the free segments of the waterline. The QPSO algorithm is used to solve this optimization problem. With view to the correlation among the design variables, the algorithm is improved by introducing the memory factor to ensure the design variables within the feasible region during the evolution process. Based on this improved QPSO algorithm, the waterlines of the full-scale hull form are approximated and the hull surface is constructed using the skinning algorithm with them. Compared with the related reference results, this approximation method has higher precision and can satisfy the engineering design requirements. It is indicated that it is feasible to represent the hull form using the reduced data. Furthermore, the success rate and the average relative difference comparison between the basic QPSO and the improved QPSO show the reliability and stability of the algorithm. This method proposed in this paper provides a foundation for the hull surface design based on the reduced data. Meanwhile, the improved QPSO algorithm can be used in the similar optimization problem with the design variables correlated with each other.

Role of carboxyl ester lipase (CEL) in pancreatic disease: Functional characterization of CEL protein variants differing in C-terminal sequence

An improved quantum-behaved particle swarm optimization (IQPSO) algorithm is employed to determine aerosol size distribution (ASD). The direct problem is solved using the anomalous diffraction approximation and Lambert–Beer's Law. Compared with the standard particle swarm optimization algorithm, the stochastic particle size optimization algorithm and the original QPSO, our IQPSO has faster convergence speed and higher accuracy within a smaller number of generations. Optimization parameters for the IQPSO were also evaluated; we recommend using four measurement wavelengths and 50 particles. Size distributions of various aerosol types were estimated using the IQPSO under dependent and independent models. Finally, experimental ASDs at different locations in Harbin were recovered using the IQPSO. All our results confirm that the IQPSO algorithm is an effective and reliable technique for estimating ASD.

Determination of the spectral complex refractive indices of microalgae cells by light reflectance-transmittance measurement

Two inverse models, namely as single and double-layer inverse models, are developed on the basis of a light scattering measurement technique to determine the spectral complex refractive indices of microalgae from the total hemispherical reflectance and transmittance measured in-situ. Combined with the Mie theory, a direct problem is solved by the finite volume method with the forward simulation of radiative transfer equation under the incidence of a collimated laser. An inverse problem is solved by an improved Quantum-behaved Particle Swarm Optimization (IQPSO) algorithm. The IQPSO algorithm shows a faster convergence speed and a higher accuracy within a smaller number of generations in retrieving the spectral complex refractive indices than the standard Particle Swarm Optimization (PSO) algorithm and the QPSO algorithm. The spectral complex refractive indices of Chlamydomonas reinhardtii at different wavelengths are estimated on the basis of the IQPSO algorithm with the single-layer inverse model. The influence of measurement errors on the inverse results is also investigated. Results indicate that IQPSO is a potential and effective optimal algorithm to estimate the spectral complex refractive indices of microalgae. The estimated results of the imaginary part of the refractive indices demonstrate a higher accuracy than those of the real part, especially with noisy measurement data. To improve the accuracy of single-layer estimated results, a double-layer inverse model is proposed. The investigation reveals that the double-layer inverse model can obtain more accurate results than the single-layer inverse model, especially with random measurement errors, which is more important for directing the experiment of retrieving optical refractive index of microalgae in the further research.

Retrieval of aerosol size distribution using improved quantum-behaved particle swarm optimization on spectral extinction measurements

An improved quantum-behaved particle swarm optimization (IQPSO) algorithm is employed to determine aerosol size distribution (ASD). The direct problem is solved using the anomalous diffraction approximation and Lambert–Beer's Law. Compared with the standard particle swarm optimization algorithm, the stochastic particle size optimization algorithm and the original QPSO, our IQPSO has faster convergence speed and higher accuracy within a smaller number of generations. Optimization parameters for the IQPSO were also evaluated; we recommend using four measurement wavelengths and 50 particles. Size distributions of various aerosol types were estimated using the IQPSO under dependent and independent models. Finally, experimental ASDs at different locations in Harbin were recovered using the IQPSO. All our results confirm that the IQPSO algorithm is an effective and reliable technique for estimating ASD.

A quantum-behaved particle swarm optimization with memetic algorithm and memory for continuous non-linear large scale problems

Quantum-behaved particle swarm optimization (QPSO), a novel variant of PSO inspired by quantum mechanics, is a global convergence guaranteed algorithm, which outperforms the original PSO in search ability and has fewer parameters to control. But as many other PSOs, it is easy to fall into local optimum in solving high-dimensional complex optimization problems. This paper proposes an improved QPSO algorithm for continuous non-linear large scale problems based on memetic algorithm and memory mechanism. The memetic algorithm is used to make all particles (each particle corresponding to a memetic) gain some experience through a local search before being involved in the evolutionary process, and the memory mechanism is used to introduce a so-called ‘bird kingdom’ with memory capacity, both of which can improve the global search ability of the algorithm. Another difference compared to the previous QPSOs is that we let each dimension of a particle update with the same random number, thus increasing the speed of convergence and enhancing the ability of local search. Numerical experiments are conducted to compare the proposed algorithm with different variants of PSO and other swarm intelligence algorithms. The experimental results show the superiority of the proposed approach on benchmark test functions.

Improved QPSO algorithm based on random evaluation and its parameter control

In order to improve the convergence performance of Quantum-behaved Particle Swarm Optimization(QPSO) algorithm,this paper proposed an improved QPSO algorithm which was called RE-QPSO based on the random evaluation strategy.The new algorithm evaluated the innovation of particles by using a random factor and improved the ability of the particles to get rid of the local optima.Fixed value strategy and linear decreasing strategy were proposed for controlingthe theunique parameter of QPSO algorithm and they were tested on six benchmark functions.According to the test results,some conclusions concerning the selection of the parameter were drawn.