Strong Global Search Capability Research Articles

Study on path planning of mechanized harvesting of ratoon rice in the first season based on the capacitated arc routing problem model.

The stubble crushing caused by the harvester during the first season of ratoon rice harvesting will directly affect the grain yield of the ratoon season. In this work, a harvester path planning method for quadrilateral fields to address the harvester driving path problem of the first season of ratoon rice mechanized harvesting is proposed. This research first analyzes the operational characteristics and requirements of ratoon rice first-season mechanized harvesting, and then models the mechanized harvesting process of ratoon rice in the first season as a capacitated arc routing problem (CARP) considering the fact that the harvester cannot complete the full-coverage harvesting operation at one time due to the limitation of grain bin volume. The genetic algorithm (GA) with strong global search capability is used to solve it, and the selection and variation links of the algorithm are improved. The path planning method proposed in this article can dynamically find the optimal harvester travel route according to the specific conditions of the field and the parameters of harvester implements. The simulation test shows that the CARP method performs better in terms of harvesting path length and crushed area compared to the conventional rectangular detour and foldback reciprocating harvesting paths. The degree of optimization of this method is influenced by various factors such as the width of the cutting table, the turning radius of the harvester, and the size of the grain bin capacity. This research provides a more efficient and flexible path planning method to improve the efficiency of ratoon rice first-season mechanized harvesting operations and optimize the harvester's operating path, which can well meet the operational requirements.

A Classification Technique for English Teaching Resources and Merging Using Swarm Intelligence Algorithm

The evolution of social education has necessitated the optimization of various teaching approaches, and the classification of English teaching resources is one of the crucial factors. With the development of electronic computers and Big Data technologies, the classification of teaching resources that could not be realized before has become possible now. However, the traditional classification methods cannot meet the requirements of modern computing due to the limitations of implementation. The emergence of swarm intelligence algorithms makes the classification of teaching resources possible. Swarm intelligence algorithm is a swarm-based multipoint random search algorithm, which includes evolutionary algorithm, immune algorithm, particle swarm procedure (PSO), ant colony process, artificial fish swarm mechanism, and other typical intelligent techniques. The swarm intelligence algorithm has strong robustness and strong global and local search capabilities, as well as, implicit parallelism. Furthermore, it has no special requirements for objective functions and constraint functions. It has the function of “black box” and can overcome problems where traditional optimization methods are insufficient. The swarm intelligence algorithm has a large space for development and rich forms of expression, and there is an essential connection between them, so that they can be well integrated. The key goal of this study is to implement the swarm intelligence algorithm to the task of classifying English teaching resources and to provide a reference for optimizing the English teaching model. The experimental findings demonstrate that the suggested classification model for English teaching resources has excellent performance, is favorable to enhancing the utilization rate of teaching resources, and is applicable to other disciplines.

A Hybrid Sparrow Search Algorithm of the Hyperparameter Optimization in Deep Learning

Deep learning has been widely used in different fields such as computer vision and speech processing. The performance of deep learning algorithms is greatly affected by their hyperparameters. For complex machine learning models such as deep neural networks, it is difficult to determine their hyperparameters. In addition, existing hyperparameter optimization algorithms easily converge to a local optimal solution. This paper proposes a method for hyperparameter optimization that combines the Sparrow Search Algorithm and Particle Swarm Optimization, called the Hybrid Sparrow Search Algorithm. This method takes advantages of avoiding the local optimal solution in the Sparrow Search Algorithm and the search efficiency of Particle Swarm Optimization to achieve global optimization. Experiments verified the proposed algorithm in simple and complex networks. The results show that the Hybrid Sparrow Search Algorithm has the strong global search capability to avoid local optimal solutions and satisfactory search efficiency in both low and high-dimensional spaces. The proposed method provides a new solution for hyperparameter optimization problems in deep learning models.

An Improved Arithmetic Optimization Algorithm and Its Application to Determine the Parameters of Support Vector Machine

The arithmetic optimization algorithm (AOA) is a new metaheuristic algorithm inspired by arithmetic operators (addition, subtraction, multiplication, and division) to solve arithmetic problems. The algorithm is characterized by simple principles, fewer parameter settings, and easy implementation, and has been widely used in many fields. However, similar to other meta-heuristic algorithms, AOA suffers from shortcomings, such as slow convergence speed and an easy ability to fall into local optimum. To address the shortcomings of AOA, an improved arithmetic optimization algorithm (IAOA) is proposed. First, dynamic inertia weights are used to improve the algorithm’s exploration and exploitation ability and speed up the algorithm’s convergence speed; second, dynamic mutation probability coefficients and the triangular mutation strategy are introduced to improve the algorithm’s ability to avoid local optimum. In order to verify the effectiveness and practicality of the algorithm in this paper, six benchmark test functions are selected for the optimization search test verification to verify the optimization search ability of IAOA; then, IAOA is used for the parameter optimization of support vector machines to verify the practical ability of IAOA. The experimental results show that IAOA has a strong global search capability, and the optimization-seeking capability is significantly improved, and it shows excellent performance in support vector machine parameter optimization.

PV Power Forecasting Based on Relevance Vector Machine with Sparrow Search Algorithm Considering Seasonal Distribution and Weather Type

Accurate photovoltaic (PV) power forecasting is indispensable to enhancing the stability of the power grid and expanding the absorptive photoelectric capacity of the power grid. As an excellent nonlinear regression model, the relevance vector machine (RVM) can be employed to forecast PV power. However, the optimization of the free parameters is still a key problem for improving the performance of the RVM. Taking advantage of the strong global search capability, good stability, and fast convergence rate of the sparrow search algorithm (SSA), this paper optimizes the parameters of the RVM by using the SSA to develop an excellent RVM (called SSA-RVM). Consequently, a novel hybrid PV power forecasting model via the SSA-RVM is proposed to perform ultra-short-term (4 h ahead) prediction. In addition, the effects of seasonal distribution and weather type on PV power are fully considered, and different seasonal prediction models are established separately to improve the prediction capability. The benchmark is used to verify the accuracy of the SSA-RVM-based forecasting model under various conditions, and the experiment results demonstrate that the proposed SSA-RMV method outperforms the traditional RVM and support vector machine models, and it even shows a better prediction effect than the RVM models with other optimization approaches.

Distributed Integrated Synthetic Adaptive Multi-Objective Reactive Power Optimization

Reactive power is the core problem of voltage stability and economical operation in power systems. Aiming at the problem that multi-objective normalization reactive power optimization function is dependent on weight, an integrated synthesis of adaptive multi-objective particle swarm optimization (ISAMOPSO) is proposed to achieve weight adaptive. Through seven test functions and three algorithm comparison experiments, it is proved that the ISAMOPSO algorithm has stronger global search capability and better convergence. Considering the optimal access position and capacity of distributed generation (DG), the ISAMOPSO algorithm is used for three-objective reactive power optimization. Finally, the results indicate that the ISAMOPSO algorithm can not only provide a variety of optimization schemes to meet different needs, but also realize dynamic reactive power optimization, which further proves that the algorithm can provide effective technical support for solving reactive power optimization problems in practical engineering.

Research on Lane Change Motion Planning Steering Input Based on Optimal Control Theory

Aiming at traditional control methods are not suitable for solving the multiconstraint problem of the vehicle steering and lane-change maneuvering process, a hybrid optimization scheme based on dynamic adaptive salp swarm algorithm and Gaussian pseudospectral method (DASSA-GPM) is proposed for its strong global search capability. Based on a steering inverse dynamics model, the lane change motion planning steering problem is converted into an optimal control problem which is then converted into a nonlinear programming problem by applying the adaptive salp group algorithm which is solved through the sequential quadratic programming (SQP) method finally. The simulation results verify the effectiveness and feasibility of the proposed dynamic adaptive salp swarm algorithm and hybrid optimization scheme. In addition, compared with GPM which the absolute error of steering wheel angle is 8.2 × 10−4 degree, the proposed method has higher computational accuracy, which absolute error of steering wheel angle is 5.5 × 10−4 degree. At the same time, under the same calculation accuracy (10−3), compared with GPM which CPU time is 4.81 s, the proposed method has higher calculation efficiency which CPU time is 3.86 s when solving non-smooth problems. The proposed method can provide a reference value into the active safety of manned and unmanned vehicles.

To optimize well pattern during miscible gas injection process via heuristic techniques

To detect optimum well locations for new injection wells one must generally notice to existing well locations, reservoir shape, capital cost per well construction in each area as well as recovery increase relevant to new well layout. Designing injection process presents inverse optimization issue whereas adjusting exploitation wells prior to primary recovery exhibits classic optimization problem. Most important design factor namely injection well layout has no unique explicit solution to solve via analytical or numerical techniques thus one can propose meta or hyper heuristic approaches to solve such problems. Our main novelty in this work is that here we cooperate four heuristic optimization approaches with our self made fast simulation technique to optimize well locations for two exemplar miscible gas injection processes. We employ MATLAB software to construct artificial porous media prototypes with different uncertainties to investigate optimization technique performances in low rather than high risk media. Outcomes illustrate that approaches like artificial immune system or particle swarm optimization which exhibit strong local search can effectively detect optimal well locations in high risk porous media. Results also show that solution techniques such as Monte Carlo that have strong global search capabilities at expense of weak local search can discover optimum well locations only in media with low uncertainty.

An Improved Chaos Sparrow Search Optimization Algorithm Using Adaptive Weight Modification and Hybrid Strategies

Sparrow Search Algorithm (SSA) is a kind of novel swarm intelligence algorithm, which has been applied in-to various domains because of its unique characteristics, such as strong global search capability, few adjustable parameters, and a clear structure. However, the SSA still has some inherent weaknesses that hinder its further development, such as poor population diversity, weak local searchability, and falling into local optimal easily. This manuscript proposes an improved chaos sparrow search optimization algorithm (ICSSOA) to overcome the mentioned shortcomings of the standard SSA. Firstly, the Cubic chaos mapping is introduced to increase the population diversity in the initialization stage. Then, an adaptive weight is employed to automatically adjust the search step for balancing the global search performance and the local search capability in different phases. Finally, a hybrid strategy of Levy flight and reverse learning is presented to perturb the position of individuals in the population according to the random strategy, and a greedy strategy is utilized to select individuals with higher fitness values to decrease the possibility of falling into the local optimum. The experiments are divided into two modules. The former investigates the performance of the proposed approach through 20 benchmark functions optimization using the ICSSOA, standard SSA, and other four SSA variants. In the latter experiment, the selected 20 functions are also optimized by the ICSSOA and other classic swarm intelligence algorithms, namely ACO, PSO, GWO, and WOA. Experimental results and corresponding statistical analysis revealed that only one function optimization test using the ICSSOA was slightly lower than the CSSOA and the WOA among the 20-function optimization. In most cases, both the solution accuracy and convergence speed are higher than other algorithms. It further indicates that the ICSSOA has an outstanding ability to jump out of the local optimum.

Integrating Hoisting Efficiency into Construction Site Layout Plan Model for Prefabricated Construction

Construction site layout plans (CSLP) are vitally significant to prefabricated construction project management; in particular, abundant hoisting operations reinforce the importance of the location of tower cranes and precast component supply points. However, previous studies on CSLP have been conducted on traditional construction sites, and there is a gap in the research regarding the prefabricated construction site layout plan. This paper attempts to establish a comprehensive CSLP model to optimize the facility layout problem on prefabricated construction sites, integrating the hoisting efficiency of precast components into previous studies on CSLP considering safety risks and transportation cost. To solve the proposed multiobjective model, the Genetic algorithms (GAs) is applied to obtain Pareto optimal solutions due to its strong global search capability. To verify the feasibility and effectiveness of the proposed model, a prefabricated residential building project is studied as a case study. This paper attempts to improve the CSLP model for the scene of prefabricated construction in the preconstruction stage to optimize safety risk, transportation cost, and hoisting efficiency, which may facilitate future research attempting to improve the prefabricated construction site layout considering more complex actual situations.

Photonic crystal topological design for polarized and polarization-independent band gaps by gradient-free topology optimization.

Photonic crystals can be adopted to control light propagation due to their superior band gap feature. It is well known the band gap feature of photonic crystals depends significantly on the topological design of the lattices, which is rather challenging due to the highly nonlinear objective function and multiple local minima feature of such design problems. To this end, this paper proposed a new band-gap topology optimization framework for photonic crystals considering different electromagnetic wave polarization modes. Based on the material-field series-expansion (MFSE) model and the dielectric permittivity interpolation scheme, the lattice topologies are represented by using a small number of design variables. Then, a sequential Kriging-based optimization algorithm, which shows strong global search capability and requires no sensitivity information, is employed to solve the band gap design problem as a series of sub-optimization problems with adaptive-adjusting design spaces. Numerical examples demonstrated the effectiveness of the proposed gradient-free method to maximize the band gap for transverse magnetic field (TM), transverse electric field (TE), and complete modes. Compared with previously reported designs, the present results exhibit less dependency on the guess of the initial design, larger band gaps and some interesting topology configurations.

Application of Coupled LDA–KPCA and BO–MKRVM Model to Predict Coal and Gas Outbursts

This paper proposed a coupled model of effective feature extraction and optimized classifier, which can overcome the existing problems of coal and gas outbursts classification in the literatures. Firstly, we support the use of kernel principal component analysis and linear discriminant analysis methods to extract linear and nonlinear feature information from coal and gas outbursts influencing factors. Secondly, in order to realize the complementarity and correlation between coal and gas outbursts influencing factors, we perform parallel feature fusion to combine the extracted linear and nonlinear feature information. Thirdly, an improved classifier called BO–MKRVM has been proposed that combines mixed kernel relevance vector machine (MKRVM) and Bayesian optimization (BO) algorithm to predict coal and gas outbursts according to the extracted features. In BO–MKRVM model, an effective mixed kernel function which combines Gaussian kernel function and Sigmoid kernel function is proposed to improve the learning and generalization ability of the MKRVM classifier, then the BO and tenfold cross-validation are utilized to optimize kernel parameters and weight coefficients of MKRVM with strong global and local search capability, the proposed BO–MKRVM classifier is performed on coal and gas outbursts dataset. Compared with the single feature extraction method, the combination of linear and non-linear feature extraction methods can obtain complete feature information and contribute to the classification performance of outbursts. The mixed kernel function considers the characteristics of coal and gas outbursts sample data, which can also effectively improve the outbursts accuracy. After the MKRVM classifier is optimized by BO algorithm, the BO–MKRVM classifier has better fitting effect and generalization ability, and obtains higher accuracy with a lower time. The experimental results are obviously better than those of other classification models, which further verifies the applicability of the proposed coupled model.

Topological Design of Freely Vibrating Bi-Material Structures to Achieve the Maximum Band Gap Centering at a Specified Frequency

AbstractThe topological design of structures to avoid vibration resonance for a certain external excitation frequency is often desired. This paper considers the topology optimization of freely vibrating bi-material structures with fixed/varying attached mass positions, targeting at maximizing the frequency band gap centering at a specified frequency. A band gap measure index is proposed to measure the size of the band gap with a specified center frequency. Aiming at maximizing this measure index, the topology optimization problem is formulated on the basis of the material-field series-expansion (MFSE) method, which greatly reduces the number of design variables and at the same time keeps the capability to describe relatively complex structural topologies with clear boundaries. As the considered optimization problem is highly non-linear and may yield multiple local minima, a sequential Kriging-based optimization solution strategy is employed to effectively solve the optimization problem. This solution strategy exhibits a relatively strong global search capability and requires no sensitivity information. With the present topology optimization model and the gradient-free algorithm, relative large band gaps with specified center frequencies have been obtained for two-dimensional (2D) beams and three-dimensional (3D) plates, without specifying the frequency orders between which the desired band gap occurs in prior.

Multi-Objective Reactive Power Optimization Based on Improved Particle Swarm Optimization With ε-Greedy Strategy and Pareto Archive Algorithm

This paper proposes combining an improved particle swarm optimization and Pareto archive algorithm to solve the multi-objective reactive power optimization problem. The idea of $\varepsilon $ -greedy strategy is adopted and designed to improve particle swarm optimization algorithm. It makes some particles have stronger global search capability, meanwhile, others have stronger local search capability during the whole iteration process. Henceforth, the strategy significantly explores the possibility of optimal solution in local space at the early stage of the iteration, in addition, it mitigates the tendency to fall into the local optimal solution at the later stage of the iteration. The Pareto optimal solution selection problem is solved by minimizing the sum of the difference between each objective function and its optimal solution. The proposed approach is tested on IEEE39-bus and IEEE118-bus system, and it is demonstrated that the proposed approach not only restores the nodes voltage to the normal range and achieves better value for each objective function, but also outperforms other algorithms including standard particle swarm optimization and non-dominated sorting genetic algorithm II(NSGA-II).

Solving Power Economic Dispatch Problem with a Novel Quantum-Behaved Particle Swarm Optimization Algorithm

This paper proposes the shrink Gaussian distribution quantum-behaved optimization (SG-QPSO) algorithm to solve economic dispatch (ED) problems from the power systems area. By shrinking the Gaussian probability distribution near the learning inclination point of each particle iteratively, SG-QPSO maintains a strong global search capability at the beginning and strengthen its local search capability gradually. In this way, SG-QPSO improves the weak local search ability of QPSO and meets the needs of solving the ED optimization problem at different stages. The performance of the SG-QPSO algorithm was obtained by evaluating three different power systems containing many nonlinear features such as the ramp rate limits, prohibited operating zones, and nonsmooth cost functions and compared with other existing optimization algorithms in terms of solution quality, convergence, and robustness. Experimental results show that the SG-QPSO algorithm outperforms any other evaluated optimization algorithms in solving ED problems.

Wind driven dragonfly algorithm for global optimization

SummaryDragonfly algorithm (DA) is a new swarm intelligence optimization algorithm based on the static and dynamic swarm behavior of dragonflies. The algorithm has the characteristics of simple structure, strong search ability, easy implementation, and strong robustness. However, the DA algorithm itself also has insufficient solution accuracy and slow convergence speed. The Wind Driven Optimization algorithm (WDO) has the characteristics of fast convergence speed and strong global search capability. So as to improve the optimization performance of the DA algorithm and avoid premature convergence, the speed of the WDO is introduced into the later calculation of the algorithm iteration, which speeds up the convergence speed of the global optimal solution. This paper proposes a dragonfly algorithm based on wind driven (WDDA), that is to reduce the blindness of the dragonfly algorithm search, improve the solution accuracy and convergence speed, to improve the overall optimization performance of the algorithm. The 23 benchmark test functions and one engineering example for optimization and comparison experiments. The experimental results show that WDDA algorithm has better performance in function optimization.

Guide Circle-based Improved Ant Colony Algorithm

In order to solve the path planning problem of autonomous mobile robots in an unknown environment, the traditional ant colony algorithm and the guide circle algorithm were combined, and an improved ant colony algorithm based on the guide circle was proposed. In terms of local path planning, the guide circle algorithm is used to avoid obstacles, which effectively solved the problem of unstable path planning ability in narrow spaces. In the aspect of global path planning, the concept of safety index circle is introduced according to the length between robot and obstacle, and partial heuristic information of ant colony algorithm is improved, so as to avoid the traditional ant colony algorithm falling into local optimal. It is verified by simulation experiments that the improved algorithm has a stronger global search capability, faster convergence speed, and enhanced path planning capability.

Complex permittivity inversion algorithm with adaptive learning strategy and parameter balancing mechanism

In this paper, we studied an adaptive learning particle swarm optimization (ALPSO) algorithm for complex permittivity inversion problem. Adaptive learning strategy and parameter balancing mechanism were used in the algorithm. The candidate particle generated in the adaptive learning strategy competed with the global optimal particle, which enabled the population to maintain strong global search capability and high search accuracy. A parameter balancing mechanism was proposed for complex permittivity inversion of low-loss materials. The inversion results of three samples showed that ALPSO not only had fast convergence speed but also was not easy to fall into the local optimum. The objective function improvement rate was 10.91 %, 1.21 % and 37.53 %, respectively. The reflectivity measurement results of the composite sample were in good agreement with the theoretical calculation results, which proved the correctness of the inversion results.

Thrust Performance Improvement for PMSLM Through Double-Layer Reverse Skewed Coil and WRF-MKH Method

This article is dedicated to achieving the best thrust performance of high thrust and low thrust ripple of permanent magnet synchronous linear motors (PMSLMs), which are used in precision positioning system. To achieve this, a novel PMSLM with double-layer reverse skewed coil (DRSC) is designed and optimized. First, the topological structure of the DRSC-PMSLM is introduced, and the effectiveness of the DRSC is analyzed qualitatively according to the analytic model. Certain hypotheses that are more suited to qualitative analysis can lead to the low accuracy of the analytic model. Therefore, for the subsequent optimization, the weighted random forest (WRF), an enhanced random forest (RF) algorithm by assigning different weights to different regression trees, is proposed to fit the sample data generated by the finite element method to establish a high-precision proxy model. Accuracy test proves that the WRF model has higher accuracy than the standard RF model and the analytical model. Subsequently, a new global optimization algorithm, call the modified Krill Herd (MKH) algorithm is proposed to iteratively optimize the WRF model to obtain the optimal structure parameters. The MKH algorithm is a modified KH algorithm that two strategies are used to improve the convergence rate and avoid premature convergence. Comparative experiments are performed by using genetic algorithm, particle swarm optimization, and standard Krill herd, which prove that the MKH has faster convergence speed and stronger global search capability. The thrust performance of the optimized motor is improved considerably compared with that of the initial motor, which further proves the effectiveness of the MKH used in this article. Finally, the prototype experiment proves that the PMSLM with DRSC has the best thrust performance.

Optimal hydropower station dispatch using quantum social spider optimization algorithm

SummaryIn this article, a new quantum social spider optimization (QSSO) algorithm is proposed. In the QSSO algorithm, we introduce an encoding approach based on bits described on social spider optimization (SSO) and serves as the evolution method of the population space. For the encoding of individuals, the probability amplitude expression of quantum bit is applied to describe the position of individuals, by which one individual's position can be expressed as the superposition of multistates. In such a way, the population diversity and the global searching capability of the SSO algorithm are enhanced. The QSSO algorithm was used to optimize the hydropower station dispatch, and the calculation results show that QSSO algorithm has fast convergence, small number of tuning parameters, high calculation accuracy, stability, simple, and is easy to be implemented with strong global search capability.