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A multi-strategy enhanced moth-flame optimization algorithm for complex inverse kinematics problems in series robots

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CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS 2025年第5期28卷 1-37页

作者： Liu, Jianlin Huang, Haisong Fan, Qingsong Ma, Chi Guizhou Univ Key Lab Adv Mfg Technol Minist Educ Guiyang 550025 Guizhou Peoples R China Guizhou Equipment Mfg Digital Workshop Modeling & Guiyang 550025 Guizhou Peoples R China Huazhong Univ Sci & Technol State Key Lab Intelligent Mfg Equipment & Technol Wuhan 430074 Peoples R China Hubei Univ Automot Technol Inst Automot Engineers Shiyan 442002 Peoples R China

In this study of optimizing the dynamic characteristics of the manipulator, with the increase of motion redundancy, the complexity of solving the inverse motion problem increases significantly, making it extremely challenging to develop a general analytical solution algorithm. To address this challenge, this paper introduces a multi-strategy enhanced moth-flame optimization algorithm, DMFO. Firstly, the uneven initialization of MFO limits its comprehensive initial search capability and leads to slow convergence speed. Consequently, it lacks competitiveness in specific engineering problems. The mutation chaos strategy, quantum evolution strategy and precise elimination strategy are used to improve the formation of DMFO. Secondly, DMFO and eight meta-heuristic algorithms (GA, PSO, ABC, MFO, DE, WO, BKA, COA) are tested on CEC2017, demonstrating its superior local and global exploitation and exploration capabilities. Then these kinematics models of 4,6,7-DOF manipulators is established based on DH method, and the objective function is derived from the forward motion equation. The effectiveness of DMFO in solving the inverse kinematics of robot arms is validated through its application. Finally, the engineering verification is carried out on the manipulator UR16e, which highlights the success of DMFO in addressing the inverse motion problem of multi-DOF serial robots.

关键词： Inverse kinematics optimization algorithm Joint limits The robotic arm UR16e

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Dual-function waveform optimization algorithm of joint transmitter and receiver via W-ADPM

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SIGNAL PROCESSING 2025年 235卷

作者： Ji, Wenshuai Lin, Hangyu Liu, Tao Sun Yat Sen Univ Sch Elect & Commun Engn Shenzhen Campus Shenzhen 518107 Peoples R China

This paper proposes a novel algorithm for joint transmitter and receiver dual-function radar communication (DFRC) systems based on orthogonal frequency division multiplexing (OFDM) waveforms. The algorithm achieves a better detection performance while containing communication bit error rate (BER) performance, modulating communication information into the phase of the OFDM waveform using M-phase-shift keying (MPSK) schemes such as Binary-PSK (BPSK) and Qinary-PSK (QPSK). Subsequently, the waveform minimizes the weighted peak sidelobe level (WPSL) of the transmit waveform and the receive mismatch filter while ensuring the bit error rate (BER) condition to reduce sidelobes. Additionally, constraints are placed on constant amplitude, BER, mainlobe energy, and signal-to-noise ratio (SNR) loss. This paper employs a Weight Alternating Direction Method of Penalty (W-ADPM) network-based approach to simultaneously optimize the transmit waveform and receive mismatched filters to address these issues, achieving the desired effect. The simulation experiments demonstrate that the proposed algorithm has better convergence performance for the DFRC OFDM waveform compared to the Alternating Direction Method of Multipliers (ADMM) algorithm. Besides, the simulation experiments show that, compared to traditional matched filters, the jointly transmitted and received mismatched filters proposed in this paper provide better ISL cross-correlation performance while ensuring the BER.

关键词： Dual-function radar communication (DFRC) Orthogonal frequency-division multiplexing (OFDM) Weight peak sidelobe level (WPSL) Alternating direction method of penalty (ADPM) optimization algorithm

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Predicting density log from well log using machine learning techniques and heuristic optimization algorithm:A comparative study

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Petroleum Research 2024年第2期9卷 176-192页

作者： Mehdi Rahmati Ghasem Zargar Abbas Ayatizadeh Tanha National Iranian Drilling Company Well Logging DepartmentAhwazIran Faculty Member of Petroleum University of Technology Iran Department of Petroleum Engineering Tarbiat Modares UniversityTehranIran

In the petroleum industry,the analysis of petrophysical parameters is critical for efficient reservoir management,production optimization,development strategies,and accurate hydrocarbon reserve *** recent years,the integration of machine learning methodologies has revolutionized the field,addressing challenges in geology,geophysics,and petroleum engineering,even when confronted with limited or imperfect *** study focuses on the prediction of density logs,a pivotal factor in evaluating reservoir hydrocarbon *** is important to note that during well logging operations,log data for specific depths of interest may be missing or incorrect,presenting a significant *** tackle this issue,we employed the Adaptive Neuro-Fuzzy Inference System(ANFIS)and Artificial Neural Networks(ANN)in combination with advanced optimization algorithms,including Particle Swarm optimization(PSO),Imperialist Competitive algorithms(ICA),and Genetic algorithms(GA).These methods exhibit promising performance in predicting density logs from gamma-ray,neutron,sonic,and photoelectric log ***,our results highlight that the Genetic algorithms-based Artificial Neural Network(GA-ANN)approach outperforms all other methods,achieving an impressive Mean Squared Error(MSE)of *** comparison,ANFIS records an MSE of 0.0015,ICA-ANN 0.0090,PSO-ANN 0.0093,and ANN 0.0183.

关键词： Density log Machine learning approaches Artificial neural networks(ANN) Adaptive neuro-fuzzy inference system(ANFIS) optimization algorithm

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Incomprehensible but Intelligible-in-time logics: Theory and optimization algorithm

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KNOWLEDGE-BASED SYSTEMS 2023年第1期264卷

作者： Mirrashid, Masoomeh Naderpour, Hosein Semnan Univ Semnan Iran

The human mind is a complex biological member with unique abilities. Today, even the most advanced computers cannot do all the brain's calculations in one second. The mind is teachable, and its logic will change based on what it has learned over time. This behavior will be the base of a theory presented in this article, named Incomprehensible but Intelligible-in-time (IbI) Logics. From a mental point of view and according to human knowledge, what it has not learned is a non-logic. However, this may change through time. Meanwhile, the IbI logic is a non-logic that may become an obvious logic in the future. This article has been formed to introduce a side of science to identify IbI logic and organize the mind's scientific idioms. Based on the introduced theory, a new optimization algorithm called IbI Logics algorithm (ILA) was also presented and compared with some other algorithms. The performance of the proposed algorithm in several constrained and unconstrained examples were evaluated. The results showed the acceptable performance and potential of the ILA for optimization goals.(c) 2023 Elsevier B.V. All rights reserved.

关键词： optimization algorithm IbI logic ILA Biological inspiration Computational intelligence

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Flight Conflict Resolution Simulation Study Based on the Improved Fruit Fly optimization algorithm

IEEE JOURNAL ON MINIATURIZATION FOR AIR AND SPACE SYSTEMS

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IEEE JOURNAL ON MINIATURIZATION FOR AIR AND SPACE SYSTEMS 2024年第3期5卷 200-209页

作者： Sun, Yulong Ding, Guoshen Zhao, Yandong Zhang, Renchi Wang, Wenjun North Automat Control Technol Inst Software Dept Taiyuan 030006 Peoples R China

Due to the increasingly widespread application of unmanned aerial vehicle (UAV), the study of flight conflict resolution can effectively avoid the collision of different UAVs. First, describe flight conflict resolution as an optimization problem. Second, the improved fruit fly optimization algorithm (IFOA) is proposed. The smell concentration judgment is equal to the coordinate instead of the reciprocal of the distance in order to make the variable accessible to be negative and occur with equal probability in the defined domain. Next, introduce the limited number of searches of the Artificial Bee Colony algorithm to avoid falling into the local optimum. Meanwhile, generate a direction and distance of the fruit fly individual through roulette. Finally, the effectiveness of the algorithm is demonstrated by computational experiments on 18 benchmark functions and the simulation of the flight conflict resolution of two and four UAVs. The results show that compared with the standard fruit fly optimization algorithm, the IFOA has superior global convergence ability and effectively reduces the delay distance, which has important potential in flight conflict resolution.

关键词： optimization Autonomous aerial vehicles Statistics Sociology Standards Safety Military aircraft Flight conflict resolution fruit fly optimization algorithm (FOA) optimization algorithm path planning unmanned aerial vehicle (UAV)

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Integration of fruit fly and firefly optimization algorithm with support vector regression in estimating daily pan evaporation

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INTERNATIONAL JOURNAL OF BIOMETEOROLOGY 2024年第2期68卷 237-251页

作者： Sharafi, Milad Samadianfard, Saeed Behmanesh, Javad Prasad, Ramendra Urmia Univ Dept Water Engn Orumiyeh Iran Univ Tabriz Fac Agr Dept Water Engn Tabriz Iran Univ Fiji Sch Sci & Technol Dept Sci Lautoka Fiji

The purpose of the present study was to predict the pan evaporation values at four stations including Urmia, Makou, Mahabad, and Khoy, located in West Azerbaijan, Iran, using support vector regression (SVR), SVR coupled by fruit fly algorithm (SVR-FOA), and SVR coupled with firefly algorithm (SVR-FFA). Therefore, for the first time, this research has used the combined SVR-FOA to predict pan evaporation. For this purpose, meteorological parameters in the period of 1990-2020 were gathered and then using the Pearson's correlation coefficient, significant inputs for pan evaporation estimation were determined. The correlation evaluation of the parameters showed that the two parameters of wind speed and sunshine hours had the highest correlation with the pan evaporation values, and in addition, these parameters, as input to the models, improved the results and increased the accuracy of the models. The obtained results indicated that at Urmia station, SVR-FFA with the lowest error was the best model. The SVR-FOA also had better performance than the SVR model. Additionally, the result showed that SVR-FOA with the lowest errors had the best capability in pan evaporation estimation at other studied stations. Therefore, it was concluded that FOA with advantages such as simplicity, fewer parameters, easy adjustment, and less calculation can significantly increase the capability of independent SVR models. Hence, based on the overall results, SVR-FOA may be recommended as the most accurate method for pan evaporation estimation.

关键词： Evaporation optimization algorithm Statistical analysis Water resource management

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Enhancing neurological disease diagnostics: fusion of deep transfer learning with optimization algorithm for acute brain stroke prediction using facial images

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SCIENTIFIC REPORTS 2025年第1期15卷 1-21页

作者： Alrowais, Fadwa Alqahtani, Mohammed Khan, Jahangir Ben Miled, Achraf Albalawneh, Da'ad Alkharashi, Abdulwhab Al Zanin, Samah Marzouk, Radwa Princess Nourah Bint Abdulrahman Univ Coll Comp & Informat Sci Dept Comp Sci POB 84428 Riyadh 11671 Saudi Arabia Univ Bisha Coll Comp & Informat Technol Dept Informat Syst & Cyber Secur Bisha 61922 Saudi Arabia King Khalid Univ Appl Coll Mahayil Dept Comp Sci Abha Saudi Arabia Northern Border Univ Coll Sci Dept Comp Sci Ar Ar 73213 Saudi Arabia Univ Tabuk Univ Coll Umluj Dept Comp Sci Tabuk Saudi Arabia Saudi Elect Univ Coll Comp & Informat Dept Comp Sci Riyadh Saudi Arabia Prince Sattam Bin Abdulaziz Univ Coll Comp Engn & Sci Dept Comp Sci Kharj Saudi Arabia Princess Nourah bint Abdulrahman Univ Coll Comp & Informat Sci Dept Informat Syst Riyadh Saudi Arabia

Stroke is a main risk to life and fitness in current society, particularly in the aging population. Also, the stroke is recognized as a cerebrovascular accident. It contains a nervous illness, which can result from haemorrhage or ischemia of the brain veins, and regular mains to assorted motor and cognitive damages that cooperate with functionality. Screening for stroke comprises physical examination, history taking, and valuation of risk features like age or certain cardiovascular illnesses. Symptoms and signs of stroke include facial weakness. Even though computed tomography (CT) and magnetic resonance imaging (MRI) are standard diagnosis techniques, artificial intelligence (AI) systems have been constructed based on these methods, which deliver fast detection. AI is gaining high attention and is being combined into numerous areas with medicine to enhance the accuracy of analysis and the quality of patient care. This paper proposes an enhancing neurological disease diagnostics fusion of transfer learning for acute brain stroke prediction using facial images (ENDDFTL-ABSPFI) method. The proposed ENDDFTL-ABSPFI method aims to enhance brain stroke detection and classification models using facial imaging. Initially, the image pre-processing stage applies the fuzzy-based median filter (FMF) model to eliminate the noise in input image data. Furthermore, fusion models such as Inception-V3 and EfficientNet-B0 perform the feature extraction. Moreover, the hybrid of convolutional neural network and bidirectional long short-term memory (CNN-BiLSTM) model is employed for the brain stroke classification process. Finally, the multi-objective sailfish optimization (MOSFO)-based hyperparameter selection process is carried out to optimize the classification outcomes of the CNN-BiLSTM model. The simulation validation of the ENDDFTL-ABSPFI technique is investigated under the Kaggle dataset concerning various measures. The comparative evaluation of the ENDDFTL-ABSPFI technique portra

关键词： Fusion of transfer learning Acute brain stroke prediction Facial images optimization algorithm Image pre-processing

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An Enhanced Human Evolutionary optimization algorithm for Global optimization and Multi-Threshold Image Segmentation

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BIOMIMETICS 2025年第5期10卷 282-282页

作者： Xiang, Liang Zhao, Xiajie Wang, Jianfeng Wang, Bin Kookmin Univ Grad Sch Techno Design TED Dept Space & Culture Design Seoul 02707 South Korea Hanyang Univ Coll Design Ansan 15588 South Korea

Thresholding image segmentation aims to divide an image into a number of regions with different feature attributes in order to facilitate the extraction of image features in the context of image detection and pattern recognition. However, existing threshold image-segmentation methods suffer from the problem of easily falling into locally optimal thresholds, resulting in poor image segmentation. In order to improve the image-segmentation performance, this study proposes an enhanced Human Evolutionary optimization algorithm (HEOA), known as CLNBHEOA, which incorporates Otsu's method as an objective function to significantly improve the image-segmentation performance. In the CLNBHEOA, firstly, population diversity is enhanced using the Chebyshev-Tent chaotic mapping refraction opposites-based learning strategy. Secondly, an adaptive learning strategy is proposed which combines differential learning and adaptive factors to improve the ability of the algorithm to jump out of the locally optimum threshold. In addition, a nonlinear control factor is proposed to better balance the global exploration phase and the local exploitation phase of the algorithm. Finally, a three-point guidance strategy based on Bernstein polynomials is proposed which enhances the local exploitation ability of the algorithm and effectively improves the efficiency of optimal threshold search. Subsequently, the optimization performance of the CLNBHEOA was evaluated on the CEC2017 benchmark functions. Experiments demonstrated that the CLNBHEOA outperformed the comparison algorithms by over 90%, exhibiting higher optimization performance and search efficiency. Finally, the CLNBHEOA was applied to solve six multi-threshold image-segmentation problems. The experimental results indicated that the CLNBHEOA achieved a winning rate of over 95% in terms of fitness function value, peak signal-to-noise ratio (PSNR), structural similarity (SSIM) and feature similarity (FSIM), suggesting that it can be considered

关键词： human evolutionary optimization algorithm CEC 2017 image segmentation learning strategy optimization algorithm

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Dynamic Machine Learning Global optimization algorithm and Its Application to Aerodynamics

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JOURNAL OF PROPULSION AND POWER 2023年第4期39卷 524-539页

作者： Zhang, Zi-Qing Li, Pei-Jing Li, Qing-Kuo Dong, Xu Lu, Xin-Gen Zhang, Yan-Feng Chinese Acad Sci Inst Engn Thermophys Key Lab Light Duty Gas Turbine Beijing 100080 Peoples R China Univ Chinese Acad Sci Sch Engn Sci Beijing 100049 Peoples R China Chinese Acad Sci Inst Engn Thermophys Distributed Generat Renewable Energy Lab Beijing 100080 Peoples R China Chinese Acad Sci Inst Engn Thermophys Key Lab Light Duty Gas Turbine Beijing 100080 Peoples R China

A dynamic extreme gradient boosting (XGBoost) and MaxLIPO trust region parallel global optimization algorithm is proposed in this paper, and it is applied to the turbomachinery blade aerodynamic optimization coupled with an in-house graphics processing unit (GPU) heterogeneous accelerated compressible flow solver, AeroWhale. The algorithm combines an accurate machine learning regression model, an efficient nongradient optimization method with no hyperparameters, a dynamic update regression strategy, and double convergence criteria to achieve high optimization accuracy and efficiency. The optimization results on the test function indicate that the number of objective function calls is less than 2% of that required by a traditional genetic algorithm, which greatly reduces the optimization time. The dynamic XGBoost model ensures that the regression model accuracy near the optimum is relatively high, which is attributed to the update strategy. The error between the optimal value identified by the proposed algorithm and the theoretical value is only 0.52% after several objective function calls. Finally, the aerodynamic optimization algorithm is applied to the LS89 high-pressure turbine, and the total pressure loss is reduced by 13.16%. The sensitivity of each optimization feature to the objective function is determined, showing that the blade suction surface control point near the trailing edge has the greatest impact on aerodynamic performance.

关键词： optimization algorithm Support Vector Machine Aerodynamic Characteristics High Pressure Turbine Turbomachinery Computational Fluid Dynamics XGBoost MaxLIPO GPU High Performance Computing

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A New Energy-Aware Method for Balancing the Load on Wireless IoT Devices Using an optimization algorithm Based on Chaos Theory

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WIRELESS PERSONAL COMMUNICATIONS 2023年第3期130卷 1677-1697页

作者： Siyi, Liu Sichuan Modern Vocat Coll Sch Elect & Informat Chengdu Sichuan Peoples R China

The number of connected devices contributing to the Internet of Things (IoT) has grown exponentially due to recent developments in wireless technology. The advent of IoT adds an entirely new category of applications to current services. Since the services are regulated by contact among objects, advantages are now being enhanced by utilizing these services. Many sensors and things are installed to track one or more activities. Hence, the load balancing protocol is essential in wireless IoT device architecture. To meet the QoS needs of IoT applications, it is crucial to measure, balance, analyze, and optimize these devices. Moreover, the IoT's vast amount of data and its processing can result in network outages. Studies on load balancing have primarily been conducted on cloud-based systems, and this challenge is an NP-hard problem. Consequently, this paper suggests a new energy-aware method for balancing the load on wireless IoT devices using a biogeography-inspired algorithm named Biogeography-Based optimization (BBO) based on chaos theory. The BBO algorithm can become trapped in local optima. Chaos theory is one of the most effective techniques for improving the performance of evolutionary algorithms in terms of both the avoidance of local optimums and the rate of convergence. Therefore, the combination of these algorithms is suggested in this paper to improve the efficiency of the balancing method. The effectiveness of the method is simulated using MATLAB. Related current methods are compared to the proposed method, and the findings showed substantial improvements in delay time and load balancing using the proposed technique. The proposed method has decreased the delay time and energy consumption by 7.58% and 15% compared to other methods.

关键词： Cloud computing Wireless technology Internet of Things Chaos theory optimization algorithm Load balancing IoT

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