Swarm Intelligence Optimization Algorithm Research Articles

Predicting dynamic compressive strength of frozen-thawed rocks by characteristic impedance and data-driven methods

In cold regions, the dynamic compressive strength (DCS) of rock damaged by freeze-thaw weathering significantly influences the stability of rock engineering. Nevertheless, testing the dynamic strength under freeze-thaw weathering conditions is often both time-consuming and expensive. Therefore, this study considers the effect of characteristic impedance on DCS and aims to quickly determine the DCS of frozen-thawed rocks through the application of machine-learning techniques. Initially, a database of DCS for frozen-thawed rocks, comprising 216 rock specimens, was compiled. Three external load parameters (freeze-thaw cycle number, confining pressure, and impact pressure) and two rock parameters (characteristic impedance and porosity) were selected as input variables, with DCS as the predicted target. This research optimized the kernel scale, penalty factor, and insensitive loss coefficient of the support vector regression (SVR) model using five swarm intelligent optimization algorithms, leading to the development of five hybrid models. In addition, a statistical DCS prediction equation using multiple linear regression techniques was developed. The performance of the prediction models was comprehensively evaluated using two error indexes and two trend indexes. A sensitivity analysis based on the cosine amplitude method has also been conducted. The results demonstrate that the proposed hybrid SVR-based models consistently provided accurate DCS predictions. Among these models, the SVR model optimized with the chameleon swarm algorithm exhibited the best performance, with metrics indicating its effectiveness, including root mean square error (RMSE) = 3.9675, mean absolute error (MAE) = 2.9673, coefficient of determination (R2) = 0.98631, and variance accounted for (VAF) = 98.634. This suggests that the chameleon swarm algorithm yielded the most optimal results for enhancing SVR models. Notably, impact pressure and characteristic impedance emerged as the two most influential parameters in DCS prediction. This research is anticipated to serve as a reliable reference for estimating the DCS of rocks subjected to freeze-thaw weathering.

Reconstituted data-driven air conditioning energy consumption prediction system employing occupant-orientated probability model as input and swarm intelligence optimization algorithms

With recent energy and environmental crises, the energy consumption prediction of air conditioning (AC) is essential. Notably, existing general prediction systems use the air temperature, real-time AC usage, time, occupancy, and AC historical energy consumption as inputs and employ feedforward neural networks. However, such systems face certain challenges. First, the input system may be difficult to reflect the real-time interaction among the building environment, occupants, and energy system; Second, the algorithms need to be upgraded to further improve accuracy, speed up convergence, and avoid overfitting. his paper introduces a novel parameter (group occupant behavior probability, GOBP) in the input system; and uses swarm intelligence algorithms with chaotic mapping and adaptive weight adjustment as optimization with LSTM and BP as the basic algorithms. The verification results obtained from five different buildings indicate the following: (1) The novel prediction system improved prediction performance with 20.08%–78.37 % of MAPE and 3.99%–53.28 % of CV-RMSE, however, it will increase 148.70s–1065.11s of calculation time. (2) For accuracy, using GOBP will improve the 0.30%–51.87 % and 0.18%–64.77 % by using swarm intelligence optimization algorithms. (3) This paper discusses the priority of selecting suitable inputs, algorithms, and optimization to combine a whole prediction system.

A novel chaotic and neighborhood search-based artificial bee colony algorithm for solving optimization problems

With the development of artificial intelligence, numerous researchers are attracted to study new heuristic algorithms and improve traditional algorithms. Artificial bee colony (ABC) algorithm is a swarm intelligence optimization algorithm inspired by the foraging behavior of honeybees, which is one of the most widely applied methods to solve optimization problems. However, the traditional ABC has some shortcomings such as under-exploitation and slow convergence, etc. In this study, a novel variant of ABC named chaotic and neighborhood search-based ABC algorithm (CNSABC) is proposed. The CNSABC contains three improved mechanisms, including Bernoulli chaotic mapping with mutual exclusion mechanism, neighborhood search mechanism with compression factor, and sustained bees. In detail, Bernoulli chaotic mapping with mutual exclusion mechanism is introduced to enhance the diversity and the exploration ability. To enhance the convergence efficiency and exploitation capability of the algorithm, the neighborhood search mechanism with compression factor and sustained bees are presented. Subsequently, a series of experiments are conducted to verify the effectiveness of the three presented mechanisms and the superiority of the proposed CNSABC, the results demonstrate that the proposed CNSABC has better convergence efficiency and search ability. Finally, the CNSABC is applied to solve two engineering optimization problems, experimental results show that CNSABC can produce satisfactory solutions.

Improved artificial gorilla troops optimizer with chaotic adaptive parameters - application to the parameter estimation problem of mixed additive and multiplicative random error models

For mixed additive and multiplicative random error models (MAM models), due to the complex correlation between the parameters and the model power array, derivative operations will be inevitable in the actual calculation. When the observation equation is in nonlinear form, the operations will be more complicated. The swarm intelligence optimization algorithm (SIO) can effectively solve the derivative problem when estimating the nonlinear model parameters using conventional iterative algorithms. However, for different problems, the conventional SIO cannot effectively balance the ability of global and local behavior, resulting in the algorithm falling into prematureness and failing to output effective parameter information. To address the above problems, the improved artificial gorilla troops optimizer (CAGTO) algorithm with chaotic adaptive behavior is proposed. To address the problem that the population generated by the algorithm using pseudo-random numbers in the initialization population phase has poor traversability in the feasible domain, the chaotic sequence is applied to initialize the population instead of pseudo-random number generation to ensure that the population can traverse the feasible domain as much as possible and improve the global search capability of the algorithm. Adaptive parameters that vary linearly and nonlinearly with the algorithm process are constructed to balance the global search and local search ability, while accelerating the convergence speed. Two CAGTO algorithms with different parameter settings are constructed for different problems, and the experimental results show that both CAGTO algorithms can effectively solve the parameter estimation problem of MAM models with different nonlinear forms of observation equations compared with several other comparative algorithms.

Optimized deep extreme learning machine for traffic prediction and autonomous vehicle lane change decision-making

Deep learning occupies a prominent position in the field of machine learning, where researchers have introduced autoencoder into extreme learning machine (ELM) to establish deep extreme learning machine (DELM), with the aim of enabling autonomous feature learning from data. This approach has found widespread applications in various domains, including finance and electrochemistry. However, the network accuracy of DELM is sensitive to the initial weighting and bias configurations, which can be negatively impacted by random initialization. To mitigate the adverse effects of such random initialization on network performance, this study employs two swarm intelligence optimization algorithms, particle swarm optimization (PSO) and harris hawks optimization (HHO), to optimize the input weights and biases of DELM. In addition, we apply the variational mode decomposition (VMD) technique to decompose the original data, enabling a deeper exploration of latent information within the data. Subsequently, two integrated models are constructed: VMD-HHO-DELM and VMD-PSO-DELM. We apply these models to address road traffic condition prediction and autonomous lane change decision-making problems, with a specific focus on the transportation domain. Through extensive experimentation on two real-world datasets, we observe that deep learning models based on swarm intelligence optimization algorithms exhibit excellent performance across various scenarios. In the context of road traffic condition prediction, our experiments reveal superior results under different time intervals and traffic volumes compared to traditional models, all while maintaining higher operational efficiency. For the autonomous lane change decision experiment, we establish a driver dissatisfaction model that aids in formulating more reasonable lane change decisions, thus reducing the frequency of abrupt lane changes.

Big Data Swarm Intelligence Optimization Algorithm Application in the Intelligent Management of an E-Commerce Logistics Warehouse

A dynamic mutation probability formula is utilized to optimize the model. In order to solve the logistics warehouse path problem, the ant colony optimization algorithm, optimized by a genetic algorithm, is employed to construct a logistics warehouse path optimization model. This model effectively optimizes the logistics warehouse paths. Test results comparing the convergence and distribution of non-inferior solutions demonstrated that the proposed model outperforms others in terms of convergence and non-inferior solution distribution. In practical logistics warehouse optimization, applying the proposed model to optimize cargo locations can significantly enhance the effectiveness of the objective function. The optimization resulted in improvements for all four objective functions related to cargo location, with reduction rates of 10.38%, 30.88%, 51.78%, and 88.49%, respectively. For the optimization of logistics warehouse paths, the original distance was 47.6m, which was reduced to 27.8m after optimization. Consequently, the picking distance decreased by 41.60%.

Modeling and numerical simulation optimization of gain spectrum of thulium-doped broadband fiber amplifier based on cat swarm algorithm

With the development of light technology, especially the maturation of WDM/DWDM technology, the demand for optical amplification of the S-band and S+ band (1450nm~1520nm) is increasing day by day, and the energy level structure of Tm3+ has energy level transitions to meet the requirements of S-band and S+ band amplification. Although thulium ion has a very complex energy level structure, TDFA is one of the most promising optical fiber amplifiers for S and S+ bands. At the same time, with the continuous development of computer technology and mathematical theory, the optimization algorithm has been rapidly developed and widely used in recent decades, based on genetic algorithm, simulated annealing algorithm, and other traditional optimization algorithms that have been proved to get good convergence speed and optimization results. In this paper, the thulium-doped fiber amplifier's gain is optimized by using a cat swarm intelligent optimization algorithm to obtain the maximum fiber length and doping concentration.

Oryctolagus Cuniculus Algorithm and Its Application in the Inversion Method of Asteroid Spectra Reflectance Template

To improve the global optimization ability and convergence speed of the swarm intelligence algorithm, we proposed a new swarm intelligence optimization algorithm, namely the Oryctolagus cuniculus algorithm. This includes five mechanisms: the determination of safety zones, the cave escape, the agglomeration of Oryctolagus cuniculi, the maintenance of the Oryctolagus cuniculus king, and the zone competition. Each solution is represented by each Oryctolagus cuniculus’s position (including zone number and specific location number). The grass density and safety index at the location of the Oryctolagus cuniculus represents its fitness value. The determination of safety zones implies that predators such as eagles hunt Oryctolagus cuniculi in dangerous zones, and the zone without predators is considered a safety zone. The cave escape refers to the act of Oryctolagus cuniculi using a connected cave system to flee from a dangerous zone and reach a secure zone, thereby evading potential predators. We select the Oryctolagus cuniculus with higher fitness values as the king of each zone, and the Oryctolagus cuniculi gather towards the Oryctolagus cuniculus king. This mechanism ensures that Oryctolagus cuniculus mainly searches in zones with abundant grass and quickly finds the optimal solution. In the maintenance of the Oryctolagus cuniculus king, we choose the one with higher fitness values as the Oryctolagus cuniculus king. Zone competition is induced by an increase in the number of Oryctolagus cuniculi in zones with abundant grass by ordering the fitness values of each zone, and vice versa. We apply the Oryctolagus cuniculus algorithm to the inversion method of the asteroid spectra reflectance template. The experimental results show that compared with artificial rabbit optimization, this algorithm has a faster rate of convergence and better solution, effectively screens the reflectance template, and improves the Doppler difference velocimetry accuracy. In addition, the application of the Oryctolagus cuniculus algorithm to the knapsack problem also performs effectively.

Dynamic Parameter Optimization of High-Speed Pantograph Based on Swarm Intelligence and Machine Learning

Good pantograph–catenary interaction quality is a fundamental premise for ensuring stable and reliable current collection of high-speed trains, and the optimization of dynamic parameters of high-speed pantographs provides an effective approach to improve the current collection quality of the pantograph–catenary system. In this paper, with the objective of minimizing the standard deviation of the pantograph–catenary contact force, the multi-parameter joint optimization for pantograph at different filtering frequencies and running speeds was carried out by using swarm intelligence optimization algorithm and artificial neural network method. First, the selection operator in genetic algorithm (GA) was introduced into crow search algorithm (CSA), and the selective CSA was proposed, which can effectively improve the solution accuracy and convergence rate of multi-parameter optimization. Second, a radial basis function (RBF) neural network was used to construct a surrogate model of the standard deviation of contact force with respect to the running speed and pantograph dynamic parameters, and a method for optimizing the upper limit of mapping interval of the decision variables by the selective crow search algorithm (SCSA) was proposed, which effectively improves the generalization ability of the surrogate model. Finally, by combining the surrogate model and SCSA, optimization iterations for a total of 630 combined conditions such as cut-off frequency, running speed and pantograph dynamic parameters were conducted, and an optimization method for high-speed pantograph dynamic parameters with universal applicability was proposed.

Uniaxial constitutive model for fiber reinforced concrete: A physics-based data-driven framework

Fiber reinforced concrete (FRC) has improved strength and ductility, making it suitable for a wider range of engineering structures. The development of a constitutive model is crucial for analyzing mechanical behavior of these structures, while this still remains challenging as the complex composition of FRC makes it difficult to formulate explicit relationships among a range of critical material parameters. With the latest development of data and digital technologies, data-driven approaches have emerged as a powerful alternative that are capable of solving advanced and complex engineering problems. Developing data-driven methods based on objective data analysis and decision-making to predict mechanical behavior of engineering structures is an important direction of such development. In this paper, a uniaxial constitutive model of fiber-reinforced concrete is developed by a physics-based data-driven framework. The framework consists of three important parts, including construction of experimental database, parameters calibration of physical models, and implementation of neural network. From the experimental data collected from published literature, an experimental database is built first. A physical model is proposed by modifying the uniaxial constitute model for normal concrete, so that it is more convenient and straightforward to consider the effect of fibers on fiber reinforced concrete subjected to compression, tension and repeat loading conditions. The parameters of the model are calibrated against experimental data by the swarm intelligence optimization algorithms. With the calibrated parameters of the physical model, a Fully Connected Neural Network (FCNN) is trained to be used to predict the physical parameters of fiber reinforced concrete. By comparing with independent experimental data, the proposed fiber-reinforced concrete uniaxial constitutive model constructed using the physics-based data-driven framework can accurately predict the stress–strain relationships of a range of FRC, which suggests that the FRC material model can be used in the numerical simulation and design of FRC components and structures. In addition, the proposed model is applicable to multiple loading conditions.

Mapping high poplar growth areas for bioenergy cultivation: A swarm-optimized approach

Poplar trees are a renewable energy source, and identifying high-growth areas for their cultivation is essential for successful bioenergy production. This study presents a novel approach to identify these areas using support vector regression (SVR) combined with two swarm intelligence optimization algorithms, i.e., particle swarm optimization (PSO) and gray wolf optimizer (GWO). The approach uses a spatial database of thirteen geoenvironmental variables and the location of 136 poplar farms in a rugged and heterogeneous region in western Iran. The PSO and GWO algorithms optimized the hyperparameters of the base SVR model and generated two swarm-optimized models: SVR-PSO and SVR-GWO. The swarm-optimized models were compared to the standalone SVR model using various evaluation metrics. The results showed that the SVR-GWO model achieved the highest performance (accuracy = 82%, sensitivity = 82%, specificity = 74%, and prediction rate = 92%), followed by the SVR-PSO model (accuracy = 81%, sensitivity = 81%, specificity = 72%, and prediction rate = 90%), while the standalone SVR model had accuracy and stability (accuracy = 75%, sensitivity = 77%, specificity = 68%, and prediction rate = 82%). The results also revealed that elevation, slope, topographic wetness index, and proximity to roads and rivers were strongly associated with the distribution of poplar farms in the study area. This study provided a novel and effective approach for identifying high poplar growth areas that can help decision-makers and stakeholders allocate resources and plan interventions to promote poplar cultivation for renewable energy production.

Well placement optimization with a novel swarm intelligence optimization algorithm: Sparrow Search Algorithm

Well placement optimization plays a critical role in developing oil and gas reservoirs, presenting a formidable challenge due to the high dimension of the search space and computational requirements. This research addresses the simultaneous optimization of well location and flow rates for production and injection wells in a heterogeneous oil reservoir located in southwestern Iran. The objective function employed to evaluate the proposed solutions generated by the Sparrow Search Algorithm (SSA) is the net present value (NPV). To enhance the performance of SSA, a sensitivity analysis is conducted to tune its hyperparameters, while keeping the epochs and population size fixed to manage runtime effectively. The study tackles the challenges posed by reservoir heterogeneity and the time-consuming nature of the optimization process through various strategies. The decision variables are reduced to a reasonable order, and a quality index is introduced to guide the algorithm towards exploring areas of the reservoir with higher potential. Furthermore, runtime is utilized as a termination condition to optimize computational time. The inclusion of the quality map significantly improves the NPV outcomes, allowing for more effective well placement decisions. Physical constraints related to well placement are handled using a penalty method and map cleaning techniques. Having addressed these challenges, the study investigates the impact of fixed parameters on the optimization results. By conducting an optimization run with 100 epochs, comparable outcomes to the case of 50 epochs (with a 5% improvement) are achieved, albeit with a longer runtime of 20 h. However, increasing the population size substantially raises both runtime and computational costs. Therefore, runtime is considered a practical termination condition, and efficient runtimes of 10 and 15 h are selected for this specific problem. The results indicate that a population size of 25 sparrows outperforms the cases with 50 and 100 epochs, respectively, yielding slightly higher NPV values. Furthermore, the performance of SSA is compared to that of Particle Swarm Optimization (PSO) in terms of NPV, convergence, and runtime. The results highlight the advantages of SSA, demonstrating faster convergence and achieving higher NPV values compared to PSO. However, the computational cost of SSA should be taken into consideration, as it requires significantly more time to reach the optimal solution compared to PSO. Overall, this research emphasizes the high potential of the SSA algorithm for optimizing well placements and its practical relevance in real-world oil industry cases.

Advanced slime mould algorithm incorporating differential evolution and Powell mechanism for engineering design

The slime mould algorithm (SMA) is a population-based swarm intelligence optimization algorithm that simulates the oscillatory foraging behavior of slime moulds. To overcome its drawbacks of slow convergence speed and premature convergence, this paper proposes an improved algorithm named PSMADE, which integrates the differential evolution algorithm (DE) and the Powell mechanism. PSMADE utilizes crossover and mutation operations of DE to enhance individual diversity and improve global search capability. Additionally, it incorporates the Powell mechanism with a taboo table to strengthen local search and facilitate convergence toward better solutions. The performance of PSMADE is evaluated by comparing it with 14 metaheuristic algorithms (MA) and 15 improved MAs on the CEC 2014 benchmarks, as well as solving four constrained real-world engineering problems. Experimental results demonstrate that PSMADE effectively compensates for the limitations of SMA and exhibits outstanding performance in solving various complex problems, showing potential as an effective problem-solving tool.

Joint Light-Sensitive Balanced Butterfly Optimizer for Solving the NLO and NCO Problems of WSN for Environmental Monitoring.

Existing swarm intelligence (SI) optimization algorithms applied to node localization optimization (NLO) and node coverage optimization (NCO) problems have low accuracy. In this study, a novel balanced butterfly optimizer (BBO) is proposed which comprehensively considers that butterflies in nature have both smell-sensitive and light-sensitive characteristics. These smell-sensitive and light-sensitive characteristics are used for the global and local search strategies of the proposed algorithm, respectively. Notably, the value of individuals' smell-sensitive characteristic is generally positive, which is a point that cannot be ignored. The performance of the proposed BBO is verified by twenty-three benchmark functions and compared to other state-of-the-art (SOTA) SI algorithms, including particle swarm optimization (PSO), differential evolution (DE), grey wolf optimizer (GWO), artificial butterfly optimization (ABO), butterfly optimization algorithm (BOA), Harris hawk optimization (HHO), and aquila optimizer (AO). The results demonstrate that the proposed BBO has better performance with the global search ability and strong stability. In addition, the BBO algorithm is used to address NLO and NCO problems in wireless sensor networks (WSNs) used in environmental monitoring, obtaining good results.

Global path planning for airport energy station inspection robots based on improved grey wolf optimization algorithm

Global path planning is one of the key technologies for airport energy station inspection robots to achieve autonomous navigation. Due to the complexity of airport energy station buildings with numerous mechanical and electrical equipment and narrow areas, planning an optimal global path remains a challenge. This paper aimed to study global path planning for airport energy station inspection robots using an improved version of the Grey Wolf Optimizer (IGWO) algorithm. Firstly, the initialization process of the Grey Wolf Optimizer algorithm selects several grey wolf individuals closer to the optimal solution as the initial population through the lens imaging reverse learning strategy. The algorithm introduces nonlinear convergence factors in the control parameters, and adds an adaptive adjustment strategy and an elite individual reselection strategy to the location update to improve the search capability and to avoid falling into local optima. Benchmark function and global path planning simulation experiments were carried out in MATLAB to test the proposed algorithm’s effectiveness. The results showed that compared to other swarm intelligent optimization algorithms, the proposed algorithm outperforms them in terms of higher convergence speed and optimization accuracy. Friedman’s test ranked this algorithm first overall. The algorithm outperforms others in terms of average path length, standard deviation of path length, and running time.

Feature Selection and Support Vector Machine Classification method for Banknote Dirtiness Recognition Based on Marine Predator Algorithm with Mathematical Functions

Dealing with classification problems requires the crucial step of feature selection (FS), which helps to reduce data dimensions and shorten classification time. Feature selection and support vector machines (SVM) classification method for banknote dirtiness recognition based on marine predator algorithm (MPA) with mathematical functions was proposed. The mathematical functions were mainly used to improve the optimizatio of MPA for feature parameter selection, and the loss function and kernel function parameters of the SVM are optimized by slime mold optimization algorithm (SMA) and marine predator algorithm. According to the experimental results, the accuracy of identifying dirtiness on the entire surface of the banknote reaches 89.07%. At the same time, according to the image pattern distribution of the banknoteS, the white area image in the middle left of the collected banknote is selected by the same method to select the feature parameters and identify the dirtiness of the banknoteS. The accuracy of dirtiness recognition in the middle left white area reached 86.67%, this shows that the white area in the middle left can basically completely replace the entire banknote. To confirm the effectiveness of the feature selection method, the proposed optimization method has been compared with four other swarm intelligent optimization algorithms to verify its performance. The experiment results indicate that the enhanced strategy is successful in improving the performance of MPA. Moreover, the robustness analysis proves its effectiveness.

A Comparative Study of Swarm Intelligence Metaheuristics in UKF-Based Neural Training Applied to the Identification and Control of Robotic Manipulator

This work presents a comprehensive comparative analysis of four prominent swarm intelligence (SI) optimization algorithms: Ant Lion Optimizer (ALO), Bat Algorithm (BA), Grey Wolf Optimizer (GWO), and Moth Flame Optimization (MFO). When compared under the same conditions with other SI algorithms, the Particle Swarm Optimization (PSO) stands out. First, the Unscented Kalman Filter (UKF) parameters to be optimized are selected, and then each SI optimization algorithm is executed within an off-line simulation. Once the UKF initialization parameters P0, Q0, and R0 are obtained, they are applied in real-time in the decentralized neural block control (DNBC) scheme for the trajectory tracking task of a 2-DOF robot manipulator. Finally, the results are compared according to the criteria performance evaluation using each algorithm, along with CPU cost.

End-Cloud Collaboration Navigation Planning Method for Unmanned Aerial Vehicles Used in Small Areas.

Unmanned aerial vehicle (UAV) collaboration has become the main means of indoor and outdoor regional search, railway patrol, and other tasks, and navigation planning is one of the key, albeit difficult, technologies. The purpose of UAV navigation planning is to plan reasonable trajectories for UAVs to avoid obstacles and reach the task area. Essentially, it is a complex optimization problem that requires the use of navigation planning algorithms to search for path-point solutions that meet the requirements under the guide of objective functions and constraints. At present, there are autonomous navigation modes of UAVs relying on airborne sensors and navigation control modes of UAVs relying on ground control stations (GCSs). However, due to the limitation of airborne processor computing power, and background command and control communication delay, a navigation planning method that takes into account accuracy and timeliness is needed. First, the navigation planning architecture of UAVs of end-cloud collaboration was designed. Then, the background cloud navigation planning algorithm of UAVs was designed based on the improved particle swarm optimization (PSO). Next, the navigation control algorithm of the UAV terminals was designed based on the multi-objective hybrid swarm intelligent optimization algorithm. Finally, the computer simulation and actual indoor-environment flight test based on small rotor UAVs were designed and conducted. The results showed that the proposed method is correct and feasible, and can improve the effectiveness and efficiency of navigation planning of UAVs.

An Improved Adaptive Sparrow Search Algorithm for TDOA-Based Localization

The Ultra-Wideband (UWB) indoor positioning method is widely used in areas where no satellite signals are available. However, during the measurement process of UWB, the collected data contain random errors. To alleviate the effect of random errors on positioning accuracy, an improved adaptive sparrow search algorithm (IASSA) based on the sparrow search algorithm (SSA) is proposed in this paper by introducing three strategies, namely, the two-step weighted least squares algorithm, adaptive adjustment of search boundary, and producer–scrounger quantity adaptive adjustment. The simulation and field test results indicate that the IASSA algorithm achieves significantly higher localization accuracy than previous methods. Meanwhile, the IASSA algorithm requires fewer iterations, which overcomes the problem of the long computation time of the swarm intelligence optimization algorithm. Therefore, the IASSA algorithm has advantages in indoor positioning accuracy and robustness performance.

Analytical reentry guidance framework based on swarm intelligence optimization and altitude-energy profile

Aimed at improving the real-time performance of guidance instruction generation, an analytical hypersonic reentry guidance framework is presented. The key steps of the novel guidance framework are the parameterization of reentry guidance problems and the optimization of parameters. First, a quintic polynomial function of energy was designed to describe the altitude profile. Then, according to the altitude-energy profile, the altitude, velocity, flight path angle, and bank angle were obtained analytically, which naturally met the terminal constraints. In addition, the angle of the attack profile was determined using the velocity parameter. The swarm intelligent optimization algorithms were used to optimize the parameters. The path constraints were enforced by the penalty function method. Finally, extensive simulations were carried out in both nominal and dispersed cases, and the simulation results showed that the proposed guidance framework was effective, high-precision, and robust in different scenarios.