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A novel kinematic calibration method for robot based on the Levenberg-Marquardt and improved marine predators algorithm

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MEASUREMENT 2024年 243卷

作者： Feng, Angang Zhou, Yufei Zhang, Ranfeng Zhao, Wei Li, Zhongcan Zhu, Mingchao Chinese Acad Sci Changchun Inst Opt Fine Mech & Phys Changchun 130033 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China

The task of precision manufacturing with serial robots requires high pose accuracy. Kinematic calibration with monocular camera and chessboard is a common method to improve the pose accuracy of robots. However, the inaccurate hand-eye and base-world parameters, the ease with which cameras capture incomplete checkerboard images, as well as the slow speed and low accuracy of parameter identification, can adversely affect the results of kinematic calibration. For these reasons, this study proposes corresponding solutions to address these issues. Firstly, this study establishes a kinematic error model that takes into account not only the DH parameters of the robot, but also the hand-eye and base-world parameters. This model effectively mitigates the influence of inaccuracies in the hand-eye and base-world parameters on robot calibration, eliminating the need for additional hand-eye calibration. Secondly, to enhance the robustness of parameter identification, fusion identification algorithm is introduced that combines the Levenberg-Marquardt algorithm (LM) and the improved marine predators algorithm (IMPA). This fusion method combines LM and IMPA algorithm, and has the advantages of chaos strategy and Logical opposition-based learning strategy. It can effectively address the low convergence accuracy of the LM algorithm when tackling high-order nonlinear problems, as well as the slow convergence speed of the MPA algorithm, and accurately and quickly identify kinematic parameters. Thirdly, leveraging the concept of constraints, a method is proposed to ensure that the camera captures complete images of the chessboard, guaranteeing that each measured image can be used for calibration. Finally, the proposed calibration method is verified by experiments. The experiments show that the error of the end position and orientation of the robot is effectively reduced. The mean position error along x-axis, y-axis and zaxis and the mean absolute position error decreased from 1.5071 mm,

关键词： Kinematic error model Parameter identification marine predators algorithm Pose accuracy

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Optimal Energy Harvesting of Large-Scale Wind Farm Using marine predators algorithm

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IEEE ACCESS 2022年 10卷 24995-25004页

作者： Mohamed, Rania Gamal Ebrahim, Mohamed Ahmed Alaas, Zuhair Muhammed Ahmed, M. M. R. Marg High Inst Engn & Modern Technol Dept Elect Engn Cairo 11738 Egypt Benha Univ Dept Elect Engn Fac Engn Cairo 11629 Egypt Jazan Univ Dept Elect Engn Fac Engn Jazan 45142 Saudi Arabia Helwan Univ Fac Technol & Educ Dept Elect Engn Cairo 11795 Egypt

A new optimal control strategy for the grid side converter (GSC) and rotor side converter (RSC) of a doubly-fed induction generator (DFIG) is developed in this paper using the marine predators algorithm (MPA). To accomplish this study, a comprehensive comparison between the suggested MPA-based control strategy and a well matured Particle Swarm Optimizer (PSO) to enhance transient stability of large-scale wind systems has been presented. MPA is used to determine the optimal gains of proportional-integral (PI) controllers for GSC and RSC to ensure a maximum power point tracking (MPPT) of a large-scale wind farm. The proposed optimal control strategy is analyzed and verified via a benchmark 9-MW DFIG wind farm using MATLAB/SIMULINK simulation. The attained results of the suggested MPA-PI-based controllers are compared to the conventional PI-based MPPT controllers to validate the efficacy of the developed optimal control strategy. The superiority of the proposed MPA-PI and PSO-PI-based optimal controllers over the traditional PI regulators towards enhancing the DFIG system dynamic performance has been proved. The presented MPA-PI-based control scheme has been succeeded in extracting the maximum power of the DFIG wind farm with a reduced settling time of about 1.8% and overshooting range 97% lower than the conventional controller.

关键词： Doubly fed induction generators Rotors Wind turbines Wind energy Maximum power point trackers Wind farms Voltage control Doubly fed induction generator grid side converter marine predators algorithm maximum power point tracking particle swarm optimization wind plant's performance

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Utilization of Random Vector Functional Link integrated with marine predators algorithm for tensile behavior prediction of dissimilar friction stir welded aluminum alloy joints

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JOURNAL OF MATERIALS RESEARCH AND TECHNOLOGY-JMR&T 2020年第5期9卷 11370-11381页

作者： Abd Elaziz, Mohamed Shehabeldeen, Taher A. Elsheikh, Ammar H. Zhou, Jianxin Ewees, Ahmed A. Al-qaness, Mohammed A. A. Zagazig Univ Dept Math Fac Sci Zagazig 44519 Egypt Huazhong Univ Sci & Technol State Key Lab Mat Proc Technol Wuhan 430047 Peoples R China Kafrelsheikh Univ Dept Mech Engn Fac Engn Kafrelsheikh 33516 Egypt Tanta Univ Dept Prod Engn & Mech Design Tanta 31527 Egypt Damietta Univ Dept Comp Dumyat 34511 Egypt Wuhan Univ State Key Lab Informat Engn Surveying Mapping & R Wuhan 430079 Peoples R China

Friction stir welding (FSW) method becomes an effective technique for welding dissimilar alloys such as AA2024 and AA5083 as the conventional fusion welding methods are not very suitable for welding the dissimilar alloysand cause many defects during solidification process. Since the strength of FSWedjoints depends on the process parameters, a new method was used in this study to predict the tensile behavior of friction stir welded AA5083 and AA2024 aluminum alloy joints in terms of tensile elongation (TE) and ultimate tensile strength (UTS). A new metaheuristic algorithm called marine predators algorithm (MPA) has been integrated with Random Vector Functional Link (RVFL) network to improve the prediction accuracy. Rotational speed, welding speed, tool axial force, and pin profile were used as input parameters while UTS and TE works as the corresponding outputs. The MPARVFL model was tested and validated,a great agreement was demonstrated between the experimental results and the predicted results indicating that the developed techniqueis accurate and reliable to predict the tensile behavior of welded aluminum joints. (C) 2020 The Authors. Published by Elsevier B.V.

关键词： Friction stir welding Dissimilar joints Aluminum alloys Random Vector Functional Link marine predators algorithm

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A Multi-strategy Improved Outpost and Differential Evolution Mutation marine predators algorithm for Global Optimization

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ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2023年第8期48卷 10493-10516页

作者： Zhang, Shuhan Wang, Shengsheng Dong, Ruyi Zhang, Kai Zhang, Xiaohui Jilin Univ Coll Comp Sci & Technol Changchun 130012 Peoples R China Jilin Univ Key Lab Symbol Computat & Knowledge Engn Minist Educ Changchun 130012 Peoples R China Jilin Inst Chem Technol Coll Informat & Control Engn Jilin 132022 Peoples R China Huawei Technol Co Ltd Labs 2012 Beijing 100095 Peoples R China

marine predators algorithm (MPA) is a recent efficient metaheuristic algorithm that is enlightened by the biological behavior of ocean predators and prey. This algorithm simulates the Levy and Brownian movements of prevalent foraging strategy and has been applied to many complex optimization problems. However, the algorithm has defects such as a low diversity of the solutions, ease into the local optimal solutions, and decreasing convergence speed in dealing with complex problems. A modified version of this algorithm called ODMPA is proposed based on the tent map, the outpost mechanism, and the differential evolution mutation with simulated annealing (DE-SA) mechanism. The tent map and DE-SA mechanism are added to enhance the exploration capability of MPA by increasing the diversity of the search agents, and the outpost mechanism is mainly used to improve the convergence speed of MPA. To validate the outstanding performance of the ODMPA, a series of global optimization problems are selected as the test sets, including the standard IEEE CEC2014 benchmark functions, which are the authoritative test set, three well-known engineering problems, and photovoltaic model parameters tasks. Compared with some famous algorithms, the results reveal that ODMPA has achieved better performance than its counterparts in CEC2014 benchmark functions. And in solving real-world optimization problems, ODMPA could get higher accuracy than other metaheuristic algorithms. These practical results demonstrate that the mechanisms introduced positively affect the original MPA, and the proposed ODMPA can be a widely effective tool in tackling many optimization problems.

关键词： marine predators algorithm Outpost mechanism Differential evolution Simulated annealing

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marine predators algorithm for Parameters Identification of Triple-Diode Photovoltaic Models

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IEEE ACCESS 2020年 8卷 155832-155842页

作者： Soliman, Mahmoud A. Hasanien, Hany M. Alkuhayli, Abdulaziz Menoufiya Univ Fac Engn Dept Elect Engn Shibin Al Kawm 32511 Egypt Future Univ Egypt Fac Engn & Technol Dept Elect Engn Cairo 11835 Egypt King Saud Univ Coll Engn Dept Elect Engn Riyadh 11421 Saudi Arabia

The precise electrical modeling of photovoltaic (PV) module is crucial due to the large-scale permeation of PV power plants into electric power networks. Therefore, a triple-diode photovoltaic (TDPV) model is presented to address all PV losses. However, the TDPV is mathematically modelled by a nonlinear I-V behavior, including nine-parameters that cannot be directly determined from the PVs datasheet due to the lack data offered by the PV manufacturers. This article presents a new application of the marine predators algorithm (MPA) to properly extract the electrical parameters of the TDPV model of a PV panel. The validity of theMPA-based TDPV model is widely appraised by the numerical analyses, which are carried out under various temperatures and solar irradiations. The optimal nine-parameters achieved using the MPA are compared with that realized by different optimization approaches-based PV model. For a realistic study, the numerical results and the measured data are compared for the marketable Kyocera KC200GT and Solarex MSX-60 PV panels. The efficacy of the MPA-based TDPV model is properly executed by checking its current error with that obtained from various models. With the MPA technology, a highly accurate model of any marketable PV module can be attained, which represents a new contribution to the sector of PV power systems.

关键词： marine predators algorithm photovoltaic modeling photovoltaic power systems solar energy triple-diode model

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marine predators algorithm: A nature-inspired metaheuristic

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EXPERT SYSTEMS WITH APPLICATIONS 2020年 152卷 113377-113377页

作者： Faramarzi, Afshin Heidarinejad, Mohammad Mirjalili, Seyedali Gandomi, Amir H. Illinois Inst Technol Dept Civil Architectural & Environm Engn Chicago IL 60616 USA Torrens Univ Australia Ctr Artificial Intelligence Res & Optimizat 90 Bowen Terrace Fortitude Valley Qld 4006 Australia Univ Technol Sydney Fac Engn & Informat Technol Ultimo NSW 2007 Australia

This paper presents a nature-inspired metaheuristic called marine predators algorithm (MPA) and its application in engineering. The main inspiration of MPA is the widespread foraging strategy namely Levy and Brownian movements in ocean predators along with optimal encounter rate policy in biological interaction between predator and prey. MPA follows the rules that naturally govern in optimal foraging strategy and encounters rate policy between predator and prey in marine ecosystems. This paper evaluates the MPA's performance on twenty-nine test functions, test suite of CEC-BC-2017, randomly generated landscape, three engineering benchmarks, and two real-world engineering design problems in the areas of ventilation and building energy performance. MPA is compared with three classes of existing optimization methods, including (1) GA and PSO as the most well-studied metaheuristics, (2) GSA, CS and SSA as almost recently developed algorithms and (3) CMA-ES, SHADE and LSHADE-cnEpSin as high performance optimizers and winners of IEEE CEC competition. Among all methods, MPA gained the second rank and demonstrated very competitive results compared to LSHADE-cnEpSin as the best performing method and one of the winners of CEC 2017 competition. The statistical post hoc analysis revealed that MPA can be nominated as a high-performance optimizer and is a significantly superior algorithm than GA, PSO, GSA, CS, SSA and CMA-ES while its performance is statistically similar to SHADE and LSHADE-cnEpSin. The source code is publicly available at: https: //***/afshinfaramarzi/marine-predators-algorithm, http: //***/portfolio/marinepredators-algorithm/, https://***/matlabcentral/fileexchange/74578-marine-predatorsalgorithm-mpa, and http://***/***. (C) 2020 Elsevier Ltd. All rights reserved.

关键词： marine predators algorithm Metaheuristic Stochastic optimization Global optimization Evolutionary computation Swarm intelligence

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Solving Hospital Layout Problem as Quadratic Assignment Problem by marine predators algorithm 4

Solving Hospital Layout Problem as Quadratic Assignment Prob...

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4th International Mobile, Intelligent, and Ubiquitous Computing Conference

作者： Moftah, Mohammad E. Ghoneimy, Mohamed MSA Univ Dept Comp Sci Cairo Egypt

ISBN: (纸本)9798350367782;9798350367775

The problem of determining the location of the hospital's department to minimize the total distance traveled by patients can be formulated as a Quadratic assignment problem (QAP), one of the most difficult and important NP-hard problems in logistics. In this paper, we will solve the hospital layout problem using a swarm intelligence metaheuristic that mimics the behavior of marine predators in search of their prey called marine predator algorithm (MPA). We do a comparison study between MPA and other metaheuristics from different categories including math-based (SHIO), human-based (WarSO), evolutionary-based (DE), and biology-based (BBOA) using a benchmark dataset "Nug-12" instance from QAPLIB to assess its suitability for this specific application. The results show that MPA could solve hospital layout problems and appear superior in achieving the best solution within a feasible time.

关键词： marine predators algorithm quadratic assignment problem hospital layout problem combinatorial problem optimization problem

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Opposition Based Local Escaping marine predators algorithm for Continuous Optimization 9th

Opposition Based Local Escaping Marine Predators Algorithm f...

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9th International Conference on Metaheuristics and Nature Inspired Computing (META)

作者： Kumar, Manish Deep, Kusum Indian Inst Technol Roorkee Roorkee Uttrakhand India

ISBN: (纸本)9783031692567;9783031692574

The marine predators algorithm (MPA) is a recently developed metaheuristic algorithm that is inspired by the foraging behavior of marine predators. It has been widely used to solve real-life optimization problems. However, it frequently gets trapped in a local optima since it is unable to have a diversified population in the early stages of optimization. To overcome the premature convergence problem of MPA, this paper introduces an improved version of the marine predators algorithm named as opposition-based local escaping marine predators algorithm (OLMPA). There are two ways in which the improvement is carried out. The first improvement uses opposition based learning (OBL) assures the diversity of solutions in the search space. The second improvement uses the local escaping operation, which creates new solutions that replace the worst solutions to estimate the best solution. These enhancements are designed to address the imbalance between exploration and exploitation. The proposed OLMPA is tested on 23 benchmark functions. The numerical and statistical experimental results show that the proposed algorithm overperformed classical MPA.

关键词： Nature Inspired Optimization Metaheuristics marine predators algorithm Local escaping operator Opposition based learning

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Optimal location and sizing of multiple PV systems in a distribution network using marine predators algorithm 15

Optimal location and sizing of multiple PV systems in a dist...

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International Conference on Applied and Theoretical Electricity (ICATE)

作者： Ghinea, Constantin Sidea, Dorian O. Eremia, Mircea Toma, Lucian Univ Politehn Bucuresti Power Engn Fac Elect Power Sytems Dept Bucharest Romania

ISBN: (纸本)9781728180359

Optimal location and sizing of renewable energy sources (RES) represents an important instrument in the efficient operation and planning of the electric networks. Solar energy is the second most used RES after wind and it contributed as an important factor, in the last two decades, to improving the European energy supply security. The main purpose of this paper is to present a mathematical model for optimal location and sizing of multiple photovoltaic (PV) systems in a distribution network using marine predators algorithm (MPA). The objective function consists in minimizing the active power losses, while maintaining all bus voltages and line currents within the acceptable range. Multiple scenarios are considered in order to integrate the daily and seasonal variations of both PV output and load demand. Finally, the solution with the lowest active power losses for a one-year period is selected. The results provided by the MPA are compared, in terms of performance with those supplied by a genetic algorithm (GA).

关键词： marine predators algorithm PV system active power losses

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Improved marine predators algorithm for feature selection and SVM optimization

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KSII TRANSACTIONS ON INTERNET AND INFORMATION SYSTEMS 2022年第4期16卷 1128-1145页

作者： Jia, Heming Sun, Kangjian Li, Yao Cao, Ning Sanming Univ Coll Informat Engn Sanming 365004 Peoples R China Northeast Forestry Univ Coll Mech & Elect Engn Harbin 150040 Peoples R China

Owing to the rapid development of information science, data analysis based on machine learning has become an interdisciplinary and strategic area. marine predators algorithm (MPA) is a novel metaheuristic algorithm inspired by the foraging strategies of marine organisms. Considering the randomness of these strategies, an improved algorithm called co-evolutionary cultural mechanism-based marine predators algorithm (CECMPA) is proposed. Through this mechanism, search agents in different spaces can share knowledge and experience to improve the performance of the native algorithm. More specifically, CECMPA has a higher probability of avoiding local optimum and can search the global optimum quickly. In this paper, it is the first to use CECMPA to perform feature subset selection and optimize hyperparameters in support vector machine (SVM) simultaneously. For performance evaluation the proposed method, it is tested on twelve datasets from the university of California Irvine (UCI) repository. Moreover, the coronavirus disease 2019 (COVID-19) can be a real-world application and is spreading in many countries. CECMPA is also applied to a COVID-19 dataset. The experimental results and statistical analysis demonstrate that CECMPA is superior to other compared methods in the literature in terms of several evaluation metrics. The proposed method has strong competitive abilities and promising prospects.

关键词： marine predators algorithm co-evolutionary cultural mechanism feature selection support vector machine hyperparameters optimization

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