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Regional power grid load has strong periodicity and randomness, and its load characteristics are affected by many factors. Traditional short-term power load-forecasting methods have certain limitations in accuracy and stability, especially when dealing with complex weather and voltage changes. To improve the prediction accuracy, this paper proposes a short-term power load-forecasting model of a regional power grid based on multi-source parameter coupling with a long short-term memory neural network (LSTM) and adopts an improved particle swarm optimization (IPSO) algorithm to optimize the LSTM network. Firstly, load characteristics under different time dimensions were analyzed, combined with meteorological factors such as daily maximum temperature, minimum temperature, rainfall, air humidity, and historical load data, multi-source data were coupled, and date types were quantified by independent thermal coding technology to ensure a reasonable model input data set. Different from traditional methods, this paper introduces real-time coupling data of intensive sensing, which makes the model more sensitive to capture the subtle characteristics of load changes. In order to further optimize the performance of the LSTM model, the IPSO algorithm, and linear difference decreasing inertia weight are introduced to improve the global optimization ability and convergence speed of the PSO algorithm and reduce the risk of local optimal solutions. Finally, the accuracy of the model is verified by the measured data of dense sensing in a regional power grid in northern China. The calculation results show that the prediction model based on multi-source parameter coupling has higher accuracy and stability in short-term load forecasting. Compared with traditional forecasting methods, the mean relative error (MAPE), the root mean square error (RMSE), and the mean absolute error (MAE) are reduced by 1.8149%, 154.0884, and 130.6769, respectively. In the typical day prediction of different seasons, the model can keep the relative error of more than 90% sampling points below 2%. The average relative error is basically less than 1%. The model proposed in this paper shows higher accuracy and stronger practical application potential compared with traditional forecasting methods, especially in voltage monitoring and power grid dispatching. [ABSTRACT FROM AUTHOR]

This paper proposes an Adaptive Grey Wolf Optimizer based on Transfer function Inertia Weight of Second-order High-pass Filter (SAGWO), aiming to overcome the limitations of Grey Wolf Optimizer (GWO) in terms of convergence speed and solution accuracy. SAGWO enhances the algorithm's dynamic adaptability and greatly speeds up convergence by incorporating the transfer function of the second-order high-pass filter into the modifications of inertia and leader wolves' weight. Furthermore, SAGWO incorporates an adaptive random search component, which significantly enhances the search range and the ability to explore globally. The performance of SAGWO is assessed and compared against three prominent Swarm Intelligence Algorithms (ALO, WOA, and EHO), along with GWO and GWO optimized by PSO. This evaluation is conducted through trials on the CEC2017 standard test set. The experimental results indicate that SAGWO outperforms in terms of both convergence speed and solution correctness. Moreover, SAGWO is utilized to address practical engineering problems such as pressure vessel design and robot gripper design, showcasing its remarkable effectiveness once more. This research enhances the theoretical development of Swarm Intelligence Algorithms and demonstrates the significant utility of SAGWO in practical applications, establishing it as a powerful instrument for scientific investigation and engineering optimization.

This paper proposes an Adaptive Grey Wolf Optimizer based on Transfer function Inertia Weight of Second-order High-pass Filter (SAGWO), aiming to overcome the limitations of Grey Wolf Optimizer (GWO) in terms of convergence speed and solution accuracy. SAGWO enhances the algorithm's dynamic adaptability and greatly speeds up convergence by incorporating the transfer function of the second-order high-pass filter into the modifications of inertia and leader wolves' weight. Furthermore, SAGWO incorporates an adaptive random search component, which significantly enhances the search range and the ability to explore globally. The performance of SAGWO is assessed and compared against three prominent Swarm Intelligence Algorithms (ALO, WOA, and EHO), along with GWO and GWO optimized by PSO. This evaluation is conducted through trials on the CEC2017 standard test set. The experimental results indicate that SAGWO outperforms in terms of both convergence speed and solution correctness. Moreover, SAGWO is utilized to address practical engineering problems such as pressure vessel design and robot gripper design, showcasing its remarkable effectiveness once more. This research enhances the theoretical development of Swarm Intelligence Algorithms and demonstrates the significant utility of SAGWO in practical applications, establishing it as a powerful instrument for scientific investigation and engineering optimization. [ABSTRACT FROM AUTHOR]

Copyright of Journal of South-Central Minzu University (Natural Science Edition) is the property of Journal of South-Central Minzu University (Natural Science Edition) Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Sine cosine algorithm (SCA) is a recently developed meta-heuristic method based on the characteristics of sine and cosine functions. However, SCA algorithm may suffer premature convergence in solving complex optimization problems. To mitigate this limitation, a modified SCA (MSCA) is developed to achieve an effective balance between exploration and exploitation. Specifically, the conversion parameter can be configured adaptively by using the individual fitness information in the evolution process to guide the individual search behavior. After that, an inertia weight is embedded in the position updating equation to further improve the search accuracy and accelerate the convergence speed. The performance of MSCA algorithm is investigated on 13 benchmark functions and the chaotic time series prediction problem. Experimental results demonstrate that MSCA algorithm is more effective than the other optimization methods. [ABSTRACT FROM AUTHOR]

Particle swarm optimization (PSO) is a meta-heuristic algorithm that can be used to solve different real-world optimization problems. However, PSO suffers from some intrinsic drawbacks, including falling into local optimal and slow convergence in the latter evolution process. To deal with these issues, a hybrid PSO with adaptive learning strategy (abbreviated as HPSO-ALS) is proposed in this paper. To start with, the chaotic opposition-learning method is employed to generate the uniformly distributed particles so as to enhance the quality of the initial swarm. Next, a self-logical mapping-based nonlinear decreasing inertia weight is formulated to trade off the global search and local search abilities. Meanwhile, different adaptive position updating strategies are leveraged to achieve better convergence according to different evolution status. In addition, inspired by the cask effect, the global worst particle (gworst) is introduced into the velocity updating to make full use of the potential information of the particles. Finally, extensive experiments on sixteen benchmark functions and CEC'13 test suite have verified the promising performance of HPSO-ALS proposed in this paper. Alternatively, HPSO-ALS is also applied to the classical traveling salesman problem (TSP) and the experimental results have further validated the effectiveness and efficiency of our proposal. [ABSTRACT FROM AUTHOR]

Particle swarm optimization (PSO) stands as a prominent and robust meta-heuristic algorithm within swarm intelligence (SI). It originated in 1995 by simulating the foraging behavior of bird flocks. In recent years, numerous PSO variants have been proposed to address various optimization applications. However, the overall performance of these variants has not been deemed satisfactory. This article introduces a novel PSO variant, presenting three key contributions: First, a novel dynamic oscillation inertia weight is introduced to strike a balance between exploration and exploitation; Second, the utilization of cosine similarity and dynamic neighborhood strategy enhances both the quality of solution and the diversity of particle populations; Third, a unique worst-best example learning strategy is proposed to enhance the quality of the least favorable solution and consequently improving the overall population. The algorithm's validation is conducted using a test suite comprised of benchmarks from the CEC2014 and CEC2022 test suites on real-parameter single-objective optimization. The experimental results demonstrate the competitiveness of our algorithm against recently proposed state-of-the-art PSO variants and well-known algorithms. [ABSTRACT FROM AUTHOR]

Particle swarm optimization (PSO) stands as a prominent and robust meta-heuristic algorithm within swarm intelligence (SI). It originated in 1995 by simulating the foraging behavior of bird flocks. In recent years, numerous PSO variants have been proposed to address various optimization applications. However, the overall performance of these variants has not been deemed satisfactory. This article introduces a novel PSO variant, presenting three key contributions: First, a novel dynamic oscillation inertia weight is introduced to strike a balance between exploration and exploitation; Second, the utilization of cosine similarity and dynamic neighborhood strategy enhances both the quality of solution and the diversity of particle populations; Third, a unique worst-best example learning strategy is proposed to enhance the quality of the least favorable solution and consequently improving the overall population. The algorithm’s validation is conducted using a test suite comprised of benchmarks from the CEC2014 and CEC2022 test suites on real-parameter single-objective optimization. The experimental results demonstrate the competitiveness of our algorithm against recently proposed state-of-the-art PSO variants and well-known algorithms.

COVID-19, also known as the Coronavirus disease-2019, is an transferrable disease that spreads rapidly, affecting countless individuals and leading to fatalities in this worldwide pandemic. The precise and swift detection of COVID-19 plays a crucial role in managing the pandemic's dissemination. Additionally, it is necessary to recognize COVID-19 quickly and accurately by investigating chest x-ray images. This paper proposed a Differential Evolution Chaotic Whale Optimization Algorithm (DECWOA) based Convolutional Neural Network (CNN) method for identifying and classifying COVID-19 chest X-ray images. The DECWOA based CNN model improves the accuracy and convergence speed of the algorithm. This method is evaluated by Chest X-Ray (CXR) dataset and attains better results in terms of accuracy, precision, sensitivity, specificity, and F1-score values of about 99.89%, 99.83%, 99.81%, 98.92%, and 99.26% correspondingly. The result shows that the proposed DECWOA based CNN model provides accurate and quick identification and classification of COVID-19 compared to existing techniques like ResNet50, VGG-19, and Multi-Model Fusion of Deep Transfer Learning (MMF-DTL) models. [ABSTRACT FROM AUTHOR]

This study addresses the challenge of accurately determining the arrival time of stress wave signals in SHPB test data processing. To eliminate human error, we introduce the time-window energy ratio method and evaluate six filters for noise reduction using box fractal dimensions. A mathematical model is established to optimize the stress equilibrium and impact process, which is solved using particle swarm optimization, resulting in the PSO-TWER method. We explore the impact of inertia weight and calculation methods on optimization outcomes, defining a stress equilibrium evaluation index. The results indicate that time-window length significantly affects arrival-time outputs, and the dynamic inertia weight factor enhances optimization convergence. The method accurately determines arrival times and effectively screens test data, providing a robust approach for SHPB test data processing. [ABSTRACT FROM AUTHOR]

Automatic bird species identification is challenging due to species diversity, image variability, and dataset limitations that often lead to model overfitting. To address these issues, this study introduces a fusion of augmentation techniques to increase dataset diversity and improve model generalization. Unlike previous approaches with fixed augmentation strategies, this study uses Adaptive Inertia Weight Particle Swarm Optimization (AIWPSO) to dynamically select effective combinations of augmentations. In the AIWPSO framework, the inertia weight function guides the optimization process toward an optimal solution. Experiments on the CVIP 2018 Bird Species Challenge dataset show that this adaptive approach significantly improves model performance, boosting training accuracy by 3% and validation accuracy by 25% over prior methods. These results highlight AIWPSO’s advantage in helping models generalize effectively across diverse bird species. Overall, this study demonstrates AIWPSO’s potential to advance automated bird species identification by optimizing data augmentation strategies and enhancing accuracy in complex classification tasks.

Path planning is a crucial technology and challenge in various fields, including robotics, autonomous systems, and intelligent transportation systems. The Particle Swarm Optimization (PSO) algorithm is widely used for optimization problems due to its simplicity and efficiency. However, despite its potential, PSO has notable limitations, such as slow convergence, susceptibility to local minima, and suboptimal efficiency, which restrict its application. This paper proposed a novel strategy called the Distance-Dependent Sigmoidal Inertia Weight PSO (DSI-PSO) algorithm to address slow convergence in path planning optimization. This innovative algorithm is inspired by neural network activation functions to achieve faster convergence. In DSI-PSO, each particle computes a distance metric and leverages a sigmoid function to adaptively update its inertia weight. Beyond improving convergence speed, this approach also addresses path-planning challenges in autonomous vehicles. In intelligent transportation systems, effective path planning enables smart vehicles to navigate, select optimal routes, and make informed decisions. The goal is to identify a collision-free path that satisfies key criteria such as shortest distance and smoothness. This methodology not only accelerates convergence but also maintains a balance between exploration and exploitation. The effectiveness of the DSI-PSO algorithm is tested using thirteen distinct unimodal and multimodal benchmark functions, serving as rigorous test cases. Additionally, the algorithm’s realworld applicability is evaluated through a smart vehicle simulation, assessing its ability to identify safe and efficient paths while minimizing overall path length. The results demonstrate the superiority of the DSI-PSO algorithm over conventional PSO approaches, with significantly enhanced convergence rates and robust optimization performance.

Addressing the challenges that unmanned underwater vehicles (UUVs) face in path planning, including complex constraints, unstable optimization algorithm performance, and unsmooth paths, this paper proposes a path planning method using an improved particle swarm optimization (PSO) algorithm. Firstly, the function simulation method is employed to construct the underwater terrain and obstacle environment. Secondly, in terms of designing optimization objectives, the paper adds to the conventional minimization of path length, optimizing changes in the unmanned underwater vehicle's attitude angle and the uniform distribution of steering node positions, making it more adaptable to actual conditions, all with a focus on reducing energy consumption. Following this, the article analyzes the effect of inertia weight in the PSO algorithm on algorithm performance, introducing improvement measures to enhance the algorithm's optimization performance. Lastly, the calculated initial path is smoothed via B-spline curves to derive the final trajectory for robot motion planning. The simulation results demonstrate that compared to traditional ant colony optimization and standard PSO algorithms, the devised path optimization method reduces overall energy consumption by 56.6% and 19.3%, respectively, presenting superior problem-solving ability and convergence performance.

An Improved binary Non‐Linear Convergent Bi‐phase Mutated Grey Wolf Optimizer (IbGWO) is proposed for solving feature selection problems with two main goals reducing irrelevant features and maximizing accuracy. We used stratified k‐fold cross‐validation that performs stratified sampling on the data to avoid overfitting problems. The fitness function used in the proposed algorithm allows choosing the solution with the minimum number of features if more than one feature has the same highest accuracy. When stratified cross‐validation is performed, the split datasets contain the same share of the feature of interest as the actual dataset. During stratified sampling, the cross‐validation result minimizes the generalization error to a considerable extent, with a smaller variance. Feature selection could be seen as an optimization problem that efficiently removes irrelevant data from high‐dimensional data to reduce computation time and improve learning accuracy. This paper proposes an improved Non‐Linear Convergent Bi‐Phase Mutated Binary Grey Wolf Optimizer (IbGWO) algorithm for feature selection. The bi‐phase mutation enhances the rate of exploitation of GWO, where the first mutation phase minimizes the number of features and the second phase adds more informative features for accurate feature selection. A non‐linear tangent trigonometric function is used for convergence to generalize better while handling heterogeneous data. To accelerate the global convergence speed, an inertia weight is added to control the position updating of the grey wolves. Feature‐weighted K‐Nearest Neighbor is used to enhance classification accuracy, where only relevant features are used for feature selection. Experimental results confirm that IbGWO outperforms other algorithms in terms of average accuracy of 0.8716, average number of chosen features of 6.13, average fitness of 0.1717, and average standard deviation of 0.0072 tested on different datasets and in terms of statistical analysis. IbGWO is also benchmarked using unimodal, multimodal, and IEEE CEC 2019 functions, where it outperforms other algorithms in most cases. Three classical engineering design problems are also solved using IbGWO, which significantly outperforms other algorithms. Moreover, the overtaking percentage of the proposed algorithm is 69.56%. [ABSTRACT FROM AUTHOR]

This study focused on enhancing the accuracy and efficiency of color detection in stained wood. Specifically, the research investigated the dyeing process of Chinese pine wood veneers. The spectral reflectance of the stained veneers was extracted and used as input data. The Extreme Learning Machine (ELM) model was employed as the predictive model to estimate the L*, a*, b* color parameters of the stained veneers. The ELM model was optimized using the Particle Swarm Optimization (PSO) algorithm, which addressed the issue of local optima by introducing non-linear inertia weights and a novel position-velocity update strategy. Evaluation of the models was based on the L*, a*, b* average absolute error metric. The results indicated that the optimized model achieved an average absolute error of 0.16, outperforming the baseline ELM model (0.68) and the ELM model optimized with the sparrow algorithm (0.37). This research contributed to improving the efficiency of wood staining production and holds implications for the field of color detection in wood materials. [ABSTRACT FROM AUTHOR]

AbstractIn a complex navigation environment, it is very important to solve the problems of multiple constraints and complex calculations in the route planning process of multiple reconnaissance unmanned aerial vehicles (UAVs) to improve the flight reconnaissance effect, and the bat algorithm (BA) with simple parameters has a certain effect on it. Aiming at the problems of unbalanced global optimization and local optimization and slow convergence speed in the later iteration of the BA in the path planning of multi-reconnaissance UAVs, a Levy-based flight search strategy and an improved bat algorithm (IBA) with changing speed are designed. In order to solve the multi-UAV route planning problem, the 3D space is reduced to a divided two-dimensional space based on the “scatter plot” method, and the spatial encoding method is used. Finally, it is proved that the IBA is theoretically more concise and effective than other algorithms.

The Grey Wolf Optimization Algorithm (GWO) replicates the leadership and foraging mechanisms of the natural grey wolf and excels in solving problems in a variety of domains. However, the algorithm tends to converge to a local optimal and has a slow convergence rate. This paper proposes an enhanced Grey Wolf optimization algorithm (HTGWO) based on hyperbolic tangent inertia weights to solve this problem. HTGWO employs inertia weights based on hyperbolic tangent functions to balance GWO’s global and local search capabilities of the GWO. HTGWO has a faster convergence rate and more accurate solutions than GWO. Five classical test functions were used to construct comparative experiments between the HWGWO and five classical intelligent optimization algorithms. The comparison results indicate that HWGWO has superior convergence speed, solution precision, and stability compared with the other five classical intelligent optimization algorithms. Moreover, experimental evidence suggests that the HWGWO is more effective at solving multimodal functions than unimodal functions. In addition, HTGWO can balance exploration and exploitation by adjusting the parameters according to the characteristics of different problems.

An episode rule mining to extract useful and important patterns or episodes from large event sequences represents the temporal implication of associating the antecedent and consequent episodes. The existing technique for mining precise-positioning episode rules from event sequences, mines serial episodes resulting in enormous memory consumption. To resolve this issue, the proposed work ensures the generation of fixed-gap episodes and parameter settings through the use of Particle Swarm Optimization mechanism. Fixed-gap episodes are generated using Natural Exponent Inertia Weight-based Particle Swarm Optimization algorithm. In this paper, a new technique called Mining Serial Episode Rules (MSER) is proposed, which utilizes the correlation between episodes and the generation of parameter selection where the occurrence time of an event is specified in the consequent. Further, a trie-based data structure to mine MSER along with a pruning technique is incorporated in the proposed methodology to improve the performance. The efficiency of the proposed algorithm MSER is evaluated on three benchmark data sets Retail, Kosarak, and MSNBC where the experimental results outperform the existing methods with respect to memory usage and execution time. [ABSTRACT FROM AUTHOR]

Background: In healthcare, machine learning is playing a significant role in computer-aided detection and diagnosis of lung nodules to reduce the load and increase the accuracy of radiologists. In the scenario where the datasize is limited, machine learning poses its advantage. The intrinsic information is discerned utilizing radiomics, which, in turn, serves as the foundational feature set for shaping the algorithmic framework employed in the classification process. In this study, the Lung Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI) database was used to study the lung nodule classification with different numbers of radiomic features selected. Materials and Methods: A total of 1018 thoracic CT scans having lung nodules categorized as "nodule ≥3 mm," "nodule <3 mm," and "non-nodule ≥ 3 mm," identified through a two-phase annotation process out of which, 300 CT scans with nodules ≥ 3 mm in size were used in our study. Four machine learning models, namely, Light GBM, Random Forest, XG Boost, and Support Vector Machine, were used with the optimum number of radiomic features selected using binary Particle Swarm Optimization combined with different topologies and time-varying inertia weights. Results and Conclusion: The XGBoost classifier with ring topology and linear decreasing inertia weight presented the best results, achieving 97.67%, 95.74%, 100%, 95.12%, 97.83%, and 98.75% for accuracy, precision, sensitivity, specificity, f1-score, and AUC, respectively. This outperformed a wide range of feature selection and machine learning approaches for lung nodule classification on the same data, as documented in numerous published academic papers. The proposed method demonstrated an improved nodule classification performance by utilizing optimal features obtained through the fusion of various inertia weights and different topologies in the feature selection method. [ABSTRACT FROM AUTHOR]

In order to improve the shortcomings of the whale optimization algorithm (WOA) in dealing with optimization problems, and further improve the accuracy and stability of the WOA, we propose an enhanced regenerative whale optimization algorithm based on gravity balance (GWOA). In the initial stage, the nonlinear time-varying factor and inertia weight strategy are introduced to change the foraging trajectory and exploration range, which improves the search efficiency and diversity. In the random walk stage and the encircling stage, the excellent solutions are protected by the gravitational balance strategy to ensure the high quality of solution. In order to prevent the algorithm from rapidly converging to the local extreme value and failing to jump out, a regeneration mechanism is introduced to help the whale population escape from the local optimal value, and to help the whale population find a better solution within the search interval through reasonable position updating. Compared with six algorithms on 16 benchmark functions, the contribution values of each strategy and Wilcoxon rank sum test show that GWOA performs well in 30-dimensional and 100-dimensional test functions and in practical applications. In general, GWOA has better optimization ability. In each algorithm contribution experiment, compared with the WOA, the indexes of the strategies added in each stage were improved. Finally, GWOA is applied to robot path planning and three classical engineering problems, and the stability and applicability of GWOA are verified. [ABSTRACT FROM AUTHOR]

One of the best performing methods in high-utility itemsets mining is based on binary particle swarm optimization. This paper aims to improve the quality of that method by finding ways to increase both the number of obtained itemsets and the associated total utility of those itemsets. The method used is based on binary particle swarm optimization, which is one of the best algorithms to obtain high-utility itemsets based on swarm intelligence. To get the maximum results we tuned up the method to determine the optimal configurations of four parameters: initial population, inertia weight, acceleration coefficients, and velocity clamping. Our experimental results on five datasets indicate that the best configuration is combining constriction factor or adaptive method for setting inertia weight and using self-adaptive for acceleration coefficient. The use of frequent itemset or transactions of the highest total utility for initializing population does not have a significant addition compared to the inertia weight and acceleration coefficient parameters. The correct configuration of the binary particle swarm optimization can increase the number of high-utility itemsets compared to the standard configuration. The percentage increase of the number of itemset compared to the existing standard method is 157.93% for chess dataset, 134.39% for connect dataset, 42.69% for accident dataset, 4.62% for foodmart dataset, and 1.58% for mushroom dataset. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Chongqing University of Technology (Natural Science) is the property of Chongqing University of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This study addresses the challenge of accurately determining the arrival time of stress wave signals in SHPB test data processing. To eliminate human error, we introduce the time-window energy ratio method and evaluate six filters for noise reduction using box fractal dimensions. A mathematical model is established to optimize the stress equilibrium and impact process, which is solved using particle swarm optimization, resulting in the PSO-TWER method. We explore the impact of inertia weight and calculation methods on optimization outcomes, defining a stress equilibrium evaluation index. The results indicate that time-window length significantly affects arrival-time outputs, and the dynamic inertia weight factor enhances optimization convergence. The method accurately determines arrival times and effectively screens test data, providing a robust approach for SHPB test data processing.

Aiming at the problem that the forward solution of Stewart platform′s position and posture is difficult to have both high accuracy and real-time,it is the key to study the kinematics of parallel platform to design a forward solution method that meets the requirements of platform accuracy and minimum calculation time.Firstly,the platform kinematics inverse solution model was established to obtain the training set of BP neural network.Secondly,the step size strategy was adjusted and the inertia weight was introduced to improve the beetle antennae search algorithm to promote the performance of the algorithm.The initial weight threshold of BP neural network was optimized through the improved algorithm,and the training was repeated to the minimum error.Finally,a section of posture change was selected for simulation verification.The simulation results show that the maximum position and posture error of the IBAS-BPNN forward solution method in the selected 100 groups of points is less than 0.6%,and the calculation time is 31.9 s.Through comparison and analysis with the BP neural network model optimized by genetic algorithm (GA),particle swarm optimization (PSO) and beetle antennae search algorithm (BAS),the superiority of IBAS-BPNN model in accuracy and calculation speed is verified.The algorithm model effectively improves the accuracy and real-time of forward position and posture solution method of Stewart platform,and provides a theoretical and methodological reference for the kinematics and control of the platform.

Abstract Whale Optimization Algorithm (WOA), as a newly proposed swarm-based algorithm, has gradually become a popular approach for optimization problems in various engineering fields. However, WOA suffers from the poor balance of exploration and exploitation, and premature convergence. In this paper, a new enhanced WOA (EWOA), which adopts an improved dynamic opposite learning (IDOL) and an adaptive encircling prey stage, is proposed to overcome the problems. IDOL plays an important role in the initialization part and the algorithm iterative process of EWOA. By evaluating the optimal solution in the current population, IDOL can adaptively switch exploitation/exploration modes constructed by the DOL strategy and a modified search strategy, respectively. On the other hand, for the encircling prey stage of EWOA in the latter part of the iteration, an adaptive inertia weight strategy is introduced into this stage to adaptively adjust the prey’s position to avoid falling into local optima. Numerical experiments, with unimodal, multimodal, hybrid and composition benchmarks, and three typical engineering problems are utilized to evaluate the performance of EWOA. The proposed EWOA also evaluates against canonical WOA, three sub-variants of EWOA, three other common algorithms, three advanced algorithms and four advanced variants of WOA. Results indicate that according to Wilcoxon rank sum test and Friedman test, EWOA has balanced exploration and exploitation ability in coping with global optimization, and it has obvious advantages when compared with other state-of-the-art algorithms.

Whale Optimization Algorithm (WOA), as a newly proposed swarm-based algorithm, has gradually become a popular approach for optimization problems in various engineering fields. However, WOA suffers from the poor balance of exploration and exploitation, and premature convergence. In this paper, a new enhanced WOA (EWOA), which adopts an improved dynamic opposite learning (IDOL) and an adaptive encircling prey stage, is proposed to overcome the problems. IDOL plays an important role in the initialization part and the algorithm iterative process of EWOA. By evaluating the optimal solution in the current population, IDOL can adaptively switch exploitation/exploration modes constructed by the DOL strategy and a modified search strategy, respectively. On the other hand, for the encircling prey stage of EWOA in the latter part of the iteration, an adaptive inertia weight strategy is introduced into this stage to adaptively adjust the prey's position to avoid falling into local optima. Numerical experiments, with unimodal, multimodal, hybrid and composition benchmarks, and three typical engineering problems are utilized to evaluate the performance of EWOA. The proposed EWOA also evaluates against canonical WOA, three sub-variants of EWOA, three other common algorithms, three advanced algorithms and four advanced variants of WOA. Results indicate that according to Wilcoxon rank sum test and Friedman test, EWOA has balanced exploration and exploitation ability in coping with global optimization, and it has obvious advantages when compared with other state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

In order to solve the problems of unstable and low efficiency in the work space, a particle swarm optimization (PSO) algorithm with dynamic changes of inertia weight and learning factors was used to optimize the trajectory of 3-5-3 piecewise polynomial interpolation. Through the kinematic analysis of the forest work vehicle, four passing points in the working space were obtained, and the 3-5-3 piecewise polynomial interpolation was used for trajectory planning. Finally, the improved particle swarm optimization algorithm was used to optimize the trajectory running time under the kinematic constraints, and the optimization performance of the standard particle swarm optimization algorithm was compared. The results showed that the 3-5-3 piecewise polynomial interpolation curve using the improved particle swarm optimization algorithm had faster convergence speed, smaller fitness after convergence, and about 13% shorter running time, which effectively improved the working efficiency of the forest work vehicle. [ABSTRACT FROM AUTHOR]

The paper presents an improved Komodo Mlipir Algorithm (KMA) with variable inertia weight and chaos mapping (VWCKMA). In contrast to the original Komodo Mlipir Algorithm (KMA), the chaotic sequence initialization population generated by Tent mapping and Tent Chaos disturbance used in VWCKMA can effectively prevent the algorithm from falling into a local optimal solution and enhance population diversity. Individuals of different social classes can be controlled by the variable inertia weight, and the convergence speed and accuracy can be increased. For the purpose of evaluating the performance of the VWCKMA, function optimization and actual predictive optimization experiments are conducted. As a result of the simulation results, the convergence accuracy and convergence speed of the VWCKMA have been considerably enhanced for single-peak, multi-peak, and fixed-dimensional complex functions in different dimensions and even thousands of dimensions. To address the nonlinearity of PM2.5 prediction in practical problems, the weights and thresholds of the BP neural network were iteratively optimized using VWCKMA, and the BP neural network was then used to predict PM2.5 using the optimal parameters. Experimental results indicate that the accuracy of the VWCKMA-optimized BP neural network model is 85.085%, which is 19.85% higher than that of the BP neural network, indicating that the VWCKMA has a certain practical application. [ABSTRACT FROM AUTHOR]

In view of the shortcomings of salp swarm algorithm (SSA), such as poor exploitation and exploration ability and low convergence accuracy, the equal amplitude and convergence periodic oscillation inertia weights were proposed to balance the exploration and exploitation ability of SSA. In order to verify the effectiveness of the improved SSA, the periodic oscillation convergence inertia weight based SSA (POCIW-SSA), moth flame optimization (MFO) algorithm, sine and cosine algorithm (SCA), grey wolf optimizer (GWO), seagull optimization algorithm (SOA) are used to optimize 24 test functions, and the statistical results are analyzed and compared. Finally, it is concluded that the improved SSA has better optimization ability. In addition, the improved SSA is applied to the design of pressure vessels, and the results show that POCIW-SSA also shows better performance in solving typical engineering problems. [ABSTRACT FROM AUTHOR]

The chaos game optimization (CGO) algorithm is a recently developed metaheuristic inspired by chaos theory and fractal configurations. In CGO, possible optimal solutions are defined as seeds and the searching process is performed using some simple equations. In this paper, weighted chaos game optimization (WCGO) is proposed and implemented to optimize engineering structures with dynamic constraints. In thismethod, an inertiaweight coefficient based on theminimum andmaximum values of the objective function is introduced to create a better balance between exploration and exploitation during the searching process. By applying the inertia weight coefficient to the seeds, their positions can be controlled accurately. To evaluate the performance of WCGO, a wide range of mathematical benchmark functions, as well as several structural design optimization problems under dynamic constraints, are computationally investigated using the new algorithm. In order to demonstrate the efficiency and robustness of WCGO, its results have been compared with those obtained by some conventional methods from the literature. Additionally, a Friedman rank test is conducted to perform a statistical study on the performance of the considered algorithms. The findings indicate thatWCGO performs better than its rivals in solving these structural optimization problems with dynamic constraints. [ABSTRACT FROM AUTHOR]

Epilepsy is a complex process and the prediction of seizure is uncertain. Currently, many researchers are exploring different optimization methods to detect the epileptic seizure. Atomic search optimization (ASO) shows a better search capability by using interaction force and the constraint force but it still some deficiency from a local optimum and a low search efficiency. To overcome these demerits, a new enhanced search ability based ASO named as ESAASO is proposed. In this study, inertia weight, levy flight and ranking strategies are integrated into ASO to improve the search performance. The proposed method has been studied on CHB-MIT scalp EEG database. There are 13 number of features are extracted by using TQWT and features are selected using genetic algorithm (GA). The proposed detection method has been computed using twelve well known optimization algorithms with LSSVM classifier. We have obtained average accuracy, sensitivity, selectivity, specificity, average detection rate, G-Mean and area under curve (AUC) values as 98.37%, 91.11%, 91.67%, 91.46%, 91.28%, 91.28% and 0.992 respectively using 10-fold cross-validation method. The proposed algorithm is found outperform as compared to conventional ASO for seizure and non-seizure detection. The proposed method can be experimented using short term EEG datasets in future.

A dissociation model suitable for the formation of gas hydrate in acidic and alkali-containing systems is established. By introducing inertia weight factor, an improved sinusoidal and cosine algorithm is obtained to optimize BP neural network and build ISCABP gas hydrate prediction model. The ISCABP model and other thermodynamic models were used to predict the hydrate formation conditions of four groups of (CH4+CO2+H2S) gas mixtures in acidic system, and the hydrate formation conditions of six groups of gas + liquid phase in alkoxide system. The temperature range is 240.15-308.45 K, and the pressure range is 0.05-82.56 mPa. The concentrations of H2S and CO2 in the mixture were 4.95-27.93% (mol) and 6.79-8.4% (mol), respectively. The results show that ISCABP hydrate model has the best predictive value. The model has strong applicability to gas field production, and can provide a theoretical basis for determining the injection amount of inhibitor and formulating the field safe operation strategy. [ABSTRACT FROM AUTHOR]

A comfortable indoor environment contributes to a better quality of life and wellbeing for its occupants. The indoor temperature, lighting, and air quality are the main controlling factors of user comfort levels. The optimum control of the lighting, air conditioners, and air ventilators helps in maximizing the user's comfort level. Nonetheless, the energy consumption of these appliances needs to be taken into consideration to minimize the operational cost and at the same time provide an environmentally friendly system. Comfort level maximization and energy consumption minimization are optimization problems. This issue is becoming more important due to the lifestyle changes caused by the COVID-19 pandemic that resulted in more time spent at home and indoors. Inertia weight artificial bee colony (IW-ABC) algorithms using linearly increasing, linearly decreasing, and exponentially increasing inertia are proposed here for the optimization of the indoor comfort index and energy usage. The multi-objective problem is tackled as a weighted single objective optimization problem. The proposed solution is tested using a dataset of 48 environmental conditions. The results of the simulation show that the IW-ABC performs better than the original ABC and other benchmark algorithms and the IW-ABC with linear increasing inertia weight has the most improved convergence behavior. [ABSTRACT FROM AUTHOR]