Artificial Bee Colony and Newton Algorithm for Forward Position Solution of Parallel Mechanism

By an organic combination of the intelligent optimization algorithm and the numerical iteration method, a general algorithm called hybrid artificial bee colony and Newton iteration (HABC-Newton) algorithm for solving the forward positions of parallel mechanism is constructed. The differential evolut...

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Main Authors: Ping Li, Siyang Peng, Linxian Che, Li Du, Junkun Hong
Format: Article
Language:zho
Published: Editorial Office of Journal of Mechanical Transmission 2019-04-01
Series:Jixie chuandong
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Online Access:http://www.jxcd.net.cn/thesisDetails#10.16578/j.issn.1004.2539.2019.04.009
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author Ping Li
Siyang Peng
Linxian Che
Li Du
Junkun Hong
author_facet Ping Li
Siyang Peng
Linxian Che
Li Du
Junkun Hong
author_sort Ping Li
collection DOAJ
description By an organic combination of the intelligent optimization algorithm and the numerical iteration method, a general algorithm called hybrid artificial bee colony and Newton iteration (HABC-Newton) algorithm for solving the forward positions of parallel mechanism is constructed. The differential evolution (DE) algorithm is incorporated into the artificial bee colony (ABC) algorithm to form a hybrid ABC (HABC) algorithm which can converge quickly to the near optimal solution of the problem. Then the optimal solution is used as initial value and Newton-Шамарский iteration method is employed to find high precision solutions. Taking 4-SPS-CU parallel mechanism kinematics analysis as an example, the forward kinematics analysis method of parallel mechanism based on HABC-Newton algorithm is stated. In order to verify the effectiveness and universality of the algorithm, two numerical examples of forward kinematics such as 4-SPS-CU and 3-RRR coupled parallel mechanisms are given. The results show that the HABC-Newton algorithm can obtain all the high accurate solutions of the parallel mechanism with less computational cost. Furthermore, comparative tests to solve these examples are carried out with HABC-Newton, ABC, DE and particle swarm optimization algorithms, and the numerical experiments indicate that HABC-Newton algorithm has better performance than compared algorithms in terms of the accuracy, robustness and computational efficiency.
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institution Kabale University
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language zho
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publisher Editorial Office of Journal of Mechanical Transmission
record_format Article
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spelling doaj-art-c68301f890ae461197fe7d7bd3c9ea232025-01-10T14:01:10ZzhoEditorial Office of Journal of Mechanical TransmissionJixie chuandong1004-25392019-04-0143445030646932Artificial Bee Colony and Newton Algorithm for Forward Position Solution of Parallel MechanismPing LiSiyang PengLinxian CheLi DuJunkun HongBy an organic combination of the intelligent optimization algorithm and the numerical iteration method, a general algorithm called hybrid artificial bee colony and Newton iteration (HABC-Newton) algorithm for solving the forward positions of parallel mechanism is constructed. The differential evolution (DE) algorithm is incorporated into the artificial bee colony (ABC) algorithm to form a hybrid ABC (HABC) algorithm which can converge quickly to the near optimal solution of the problem. Then the optimal solution is used as initial value and Newton-Шамарский iteration method is employed to find high precision solutions. Taking 4-SPS-CU parallel mechanism kinematics analysis as an example, the forward kinematics analysis method of parallel mechanism based on HABC-Newton algorithm is stated. In order to verify the effectiveness and universality of the algorithm, two numerical examples of forward kinematics such as 4-SPS-CU and 3-RRR coupled parallel mechanisms are given. The results show that the HABC-Newton algorithm can obtain all the high accurate solutions of the parallel mechanism with less computational cost. Furthermore, comparative tests to solve these examples are carried out with HABC-Newton, ABC, DE and particle swarm optimization algorithms, and the numerical experiments indicate that HABC-Newton algorithm has better performance than compared algorithms in terms of the accuracy, robustness and computational efficiency.http://www.jxcd.net.cn/thesisDetails#10.16578/j.issn.1004.2539.2019.04.009Parallel mechanismForward position solutionArtificial bee colony algorithmDifferential evolution operatorNewton iteration method
spellingShingle Ping Li
Siyang Peng
Linxian Che
Li Du
Junkun Hong
Artificial Bee Colony and Newton Algorithm for Forward Position Solution of Parallel Mechanism
Jixie chuandong
Parallel mechanism
Forward position solution
Artificial bee colony algorithm
Differential evolution operator
Newton iteration method
title Artificial Bee Colony and Newton Algorithm for Forward Position Solution of Parallel Mechanism
title_full Artificial Bee Colony and Newton Algorithm for Forward Position Solution of Parallel Mechanism
title_fullStr Artificial Bee Colony and Newton Algorithm for Forward Position Solution of Parallel Mechanism
title_full_unstemmed Artificial Bee Colony and Newton Algorithm for Forward Position Solution of Parallel Mechanism
title_short Artificial Bee Colony and Newton Algorithm for Forward Position Solution of Parallel Mechanism
title_sort artificial bee colony and newton algorithm for forward position solution of parallel mechanism
topic Parallel mechanism
Forward position solution
Artificial bee colony algorithm
Differential evolution operator
Newton iteration method
url http://www.jxcd.net.cn/thesisDetails#10.16578/j.issn.1004.2539.2019.04.009
work_keys_str_mv AT pingli artificialbeecolonyandnewtonalgorithmforforwardpositionsolutionofparallelmechanism
AT siyangpeng artificialbeecolonyandnewtonalgorithmforforwardpositionsolutionofparallelmechanism
AT linxianche artificialbeecolonyandnewtonalgorithmforforwardpositionsolutionofparallelmechanism
AT lidu artificialbeecolonyandnewtonalgorithmforforwardpositionsolutionofparallelmechanism
AT junkunhong artificialbeecolonyandnewtonalgorithmforforwardpositionsolutionofparallelmechanism