Improved Swarm Intelligence Algorithm Research Articles

Prediction and explanation of debris flow velocity based on multi-strategy fusion Stacking ensemble learning model

The debris flow velocity fundamentally determines its intensity, thereby rendering its prediction a crucial aspect of disaster prevention and control strategies. However, accurate velocity prediction has consistently posed significant challenges due to the intricate interplay of various influential factors. To address the limitations of existing models, an explainable multi-strategy fusion of Stacking ensemble learning is proposed. Initially, an improved snake optimization (ISO) algorithm is employed to adjust parameters within the model’s learners. Benchmark function comparison tests are then conducted to validate the reliability of the ISO algorithm. Subsequently, a learner selection method based on predictive performance and degree of difference is established to facilitate the selection of basic learners and meta-learners. This leads to the construction of the Stacking ensemble learning model, achieved through the integration of parameter optimization strategies from the improved swarm intelligence algorithm strategy, the error weighting strategy, and the decomposition strategy. To assess the model, a case study of the Jiangjiagou Gulley debris flow is undertaken, focusing on the prediction of the debris flow velocity. The results demonstrate high predictive accuracy, with RMSE, MAE, and MAPE values of 0.19, 0.17, and 2.46% respectively. Furthermore, under the SHAP framework, global and local explanations of the predictions are provided. Through feature importance analysis, the bed slope gradient is identified as the most crucial feature in the velocity prediction of the Jiangjiagou Gulley debris flow. Coupling effects and contributions of input features to the debris flow velocity prediction are further analyzed and explained through feature interaction analysis and single sample analysis. This study not only provides a new method for debris flow velocity prediction but also provides guiding suggestions for debris flow monitoring and control.

Distribution network insulator detection based on improved ant colony algorithm and deep learning for UAV

Distribution network insulator detection based on improved ant colony algorithm and deep learning for UAV

Real-Time Optimization of Heliostat Field Aiming Strategy via an Improved Swarm Intelligence Algorithm

Optimizing the heliostat field aiming strategy is crucial for maximizing thermal power production in solar power tower (SPT) plants while adhering to operational constraints. Although existing approaches can yield highly optimal solutions, their considerable computational cost makes them unsuitable for real-time optimization in large-scale scenes. This study introduces an efficient, intelligent, real-time optimization method based on a meta-heuristic algorithm to effectively and reliably manage SPT plant operations under varying solar conditions, such as cloud shadowing variations. To minimize redundant calculations, the real-time optimization problem is framed in a way that captures the operational continuity of the heliostat, which can be utilized to streamline the solution process. The proposed method is tested in a simulation environment that includes a heliostat field, cylindrical receiver, and cloud movement model. The results demonstrate that the algorithm presented in this paper offers higher intercept efficiency, improved robustness, and reduced optimization time in more complex scenes.

MS-IHHO-LSTM: Carbon Price Prediction Model of Multi-Source Data Based on Improved Swarm Intelligence Algorithm and Deep Learning Method

MS-IHHO-LSTM: Carbon Price Prediction Model of Multi-Source Data Based on Improved Swarm Intelligence Algorithm and Deep Learning Method

Intelligent algorithm-based analysis of corporate financial decisions in the era of cloud accounting

Abstract The development of cloud computing technology has promoted a new information technology revolution, and the arrival of the cloud accounting era has brought new ideas to enterprise financial decision-making. This paper first introduces the improved swarm intelligence algorithm grasshopper optimization algorithm optimization, using reverse learning the Levy flight mechanism to improve the global search ability of the original grasshopper algorithm, using the Gaussian variation mechanism to increase the diversity of the population to achieve algorithm optimization. The IGOA algorithm is then used to optimize the internal parameters of KELM by dynamically adjusting the optimal parameters based on the training set data through a 5-fold cross-validation analysis to evaluate the external classification performance. Then the factors affecting the financial decision analysis and the financial decision process in the cloud accounting era are introduced, and finally, some problems in traditional enterprise decision methods and the impact of intelligent algorithms on enterprise financial decisions are analyzed. In terms of the existing problems, the main ones are: 83% of them think that the traditional enterprise decision-making methods need to be innovated, and 83% of them think there is a shortage of professionals with professional financial decision-making expertise. On the impact of intelligent algorithms on enterprise financial decision-making, 88% think that the decision analysis dimension has been broadened based on intelligent algorithms, and 85% think that the decision risk has achieved fine control. The development and application of intelligent algorithms and cloud accounting technology have an important and positive effect on the financial decision-making mode of enterprises.

Retraction Note: Improved swarm intelligence algorithm for protein folding prediction

Retraction Note: Improved swarm intelligence algorithm for protein folding prediction

Design of Table Tennis Training Competition Knowledge Interaction Platform Integrating Improved Swarm Intelligence Algorithm.

Table tennis is China 's national game and the proudest sport in China's sports field. During the research and technology service work of the Chinese table tennis team for many years, it has accumulated a large amount of valuable data on the analysis of skills and tactics of training and matches, match video, training monitoring, and so on. This paper discusses the relevant theory of swarm intelligence algorithm processing big data on the table tennis training competition knowledge interaction platform system, as well as the technical support of Nginx and Tomcat, and determines the technical basis of the table tennis training competition knowledge interaction platform. Through the establishment of the firefly algorithm model, the resource search ability is enhanced, and the traditional firefly algorithm is improved. From the results of the system performance test, it can be found that the improved swarm intelligence algorithm adopted in this paper improves the global convergence, and the load balancing degree gradually decreases with the increase of time. The improved firefly algorithm shows good performance when the bandwidth is low, and the resource occupancy rate is greatly reduced. When the bandwidth is 20, it is reduced by 12.55%. It solves the shortcomings of long time and low success rate, so as to verify the convenience of the system operation and the power of functions and make the platform more intelligent and efficient.

A stacking-based ensemble learning method for earthquake casualty prediction

The estimation of the loss and prediction of the casualties in earthquake-stricken areas are vital for making rapid and accurate decisions during rescue efforts. The number of casualties is determined by various factors, necessitating a comprehensive system for earthquake-casualty prediction. To obtain accurate prediction results, an effective prediction method based on stacking ensemble learning and improved swarm intelligence algorithm is proposed in this study, which comprises three parts: (1) applying multiple base learners for training, (2) using a stacking strategy to integrate the results generated by multiple base learners to obtain the final prediction results, and (3) developing an improved swarm intelligence algorithm to optimize the key parameters in the prediction model. To verify the effectiveness of the model, we collected data pertaining to earthquake destruction from 1966 to 2017 in China. Experiments were conducted to compare the proposed method with popular machine learning methods. It was found that the stacking ensemble learning method can effectively integrate the prediction results of the base learner to improve the performance of the model, and the improved swarm intelligence algorithm can further improve the prediction accuracy. Moreover, the importance of each feature was evaluated, which has important implications for future work such as casualty prevention and rescue during earthquakes.

Task scheduling based on swarm intelligence algorithms in high performance computing environment

The high-performance computing environment is a computing platform, which aggregates multiple distributed high-performance computers from indifferent organizations, providing users with unified access and usage patterns. Since the task scheduling strategy is lack of flexibility, an optimized task scheduling model in the high-performance computing environment is proposed in this paper, which introduces an improved swarm intelligence algorithm in task queues, refines the Core Scheduler for each task, and increases the configuration of task scheduling strategy. In core task scheduling, swarm intelligence algorithm is adopted to minimize the average scheduling time for completion tasks through optimal task allocation on each node. Simulation results show that the proposed scheduling algorithm is better than the traditional task scheduling algorithm. Therefore, according to the task scheduling strategy based on swarm intelligence algorithm, it can effectively reduce the task waiting, improve the system’s throughput, the task response and system resource utilization has a better effect.

An Improved Swarm Intelligence Algorithm for Multi-Item Joint Ordering Strategy of Cruise Ship Supply

From the perspective of the global value chain of the cruise industry, the output value of cruise operation accounts for 50%, with the highest added value, and cruise ship supply is the crucial link in cruise operation management, with substantial economic benefits. Therefore, optimizing the purchasing process can not only save costs for cruise companies but also improve cruise level service. Aiming at the purchasing characteristics and modes of cruise ship supply, an optimization model of multi-item joint ordering is constructed under global cruise ship supply chain, considering different order cycles, integrated operation of purchase, delivery, and inventory based on a cruise distribution center. And an improved swarm intelligence algorithm, called fireworks algorithm with inertia weight (WFWA), is proposed for global optimization of the objective function. By comparing the optimization results with fireworks algorithm (FWA), genetic algorithm (GA), and particle swarm optimization (PSO) through experimental tests, it demonstrates that WFWA has higher optimization accuracy and better global convergence.

Application of improved intelligent ant colony algorithm in protein folding prediction

While the single ant colony algorithm and the fish swarm algorithm have many advantages, they also have various shortcomings. After analyzing the advantages and disadvantages of the ant colony algorithm and the fish swarm algorithm, this paper uses the complementary principle of the two algorithms to effectively fuse the two population intelligent algorithms. The improved swarm intelligence algorithm is applied to the well-considered protein folding prediction problem, and the simplified protein structure Toy model is verified, and the ideal results are obtained. The improved algorithm enhances the search ability, and the computational efficiency is greatly improved, ensuring the accuracy of the operation.

RETRACTED ARTICLE: Improved swarm intelligence algorithm for protein folding prediction

Swarm intelligence algorithm is to simulate the behaviour of groups of organisms to solve the target problem heuristic search algorithm. This paper briefly describes four swarm intelligence algorithms, focus on analysis of the ant colony algorithm and artificial fish algorithm and how to merge the two algorithms. Based on ACO and AFSA, a new fusion algorithm is proposed. The fusion algorithm has the robustness and parallelism of the ant colony algorithm, and has the advantages of strong global search ability and fast search speed of artificial fish swarm algorithm. The convergence of the improved fusion algorithm is proved by analysis, and the algorithm is applied to the field of protein folding prediction. It can be found that the new fuse algorithm, which in the case of find out the minimum energy value, the time required for the algorithm is less than that of the basic algorithm. It not only ensures the quality of the optimal value, but also greatly saves the calculation time. It has great practical value.

Optimal detector design for molecular communication systems using an improved swarm intelligence algorithm

The authors optimise the detection process of diffusion-based molecular communication systems utilising the weighted sum detector with appropriate weight values. Interestingly, no optimisation technique has ever been proposed for the calculation of the weights. To this end, they build on the standard particle swarm optimisation (PSO) technique and propose a robust iterative optimisation algorithm, called acceleration-aided PSO (A-APSO). While modified swarm-based optimisation algorithms focus on slight variations of the standard mathematical formulas, in A-APSO, the acceleration variable of the particles in the swarm is also involved in the search space of the optimisation problem. Particularly, they implement the A-APSO algorithm to evaluate the detector's weights that minimise the closed-form expression of the error probability. Their findings reveal that, when employing the A-APSO weights, the error performance is superior to that achieved by utilising the weight values already existing in the literature or those evaluated with the standard PSO algorithm.

The Optimization of Balancing Least Squares Influence Coefficient Method Based on Particle Swarm Optimization

On the basis of the rotor dynamic balancing theory, the rotor balancing least squares influence coefficient method has been discussed in detail in this paper. In order to solve the problem that the residual vibration in some of the measuring point and the balancing weight are comparatively large in the balance process with least squares influence coefficient method, particle swarm optimization algorithm with cross-factor, which is an improved swarm intelligence algorithm, is introduced into rotor balancing least squares influence coefficient method. Theoretical analysis and numerical examples show that the algorithm has a good convergence, with reducing the balancing weight and residual vibration effectively compared with basic least squares influence coefficient method. The result can achieve better balance effect in the rotor balancing process.

Cluster Formation based on Improved Many Optimizing Liaisons Optimization Algorithm in Wireless Sensor Networks

Extending Wireless Sensor Networks (WSNs) lifetime is one prominent challenge nowadays. Clustering in WSNs is used to manage energy consumption through data aggregation at the cluster head. WSNs energy-aware clustering is often formulated as an optimization problem. Therefore, an Improved Swarm Intelligence algorithm is proposed, named Improved Many Optimizing Liaisons (IMOL). The proposed algorithm is proposed to optimize the wireless sensor clustering to extend the network lifetime. This paper presents different comparisons among IMOL and the famous heuristic clustering algorithms. Simulation results demonstrate that the proposed IMOL algorithm gave a higher network lifetime over its comparatives.