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Time-optimal trajectory planning of serial manipulator based on adaptive cuckoo search algorithm

JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY 2021年第7期35卷 3171-3181页

作者： Zhang, Lunhui Wang, Yong Zhao, Xiaoyong Zhao, Ping He, Liangguo Hefei Univ Technol Sch Mech Engn Hefei 230009 Peoples R China

Feeding and unloading operation for part manufacturing are widely applied by industrial robots. In this paper, a set of algorithms has been used to reach higher efficiency and automation of trajectory planning for a 6-DOF integrated serial kinematic manipulator. Depending on the key nodes of the joint angles calculated by a hybrid inverse kinematics algorithm, the continuous quintic B-spline curve algorithm was utilized for planning smooth trajectories of the feeding motion from peer to peer. An adaptive cuckoo search (ACS) algorithm with high efficiency and excellent stability was proposed to minimize the total motion time under strict dynamic constraints. Comparing with 5 commonly used heuristic methods, the ACS algorithm has faster convergence speed and higher accuracy based on the same fitness function. To verify the implementation effect of the strategy, a 1:5 scale experimental platform was designed and built to implement the time-optimal trajectories. The simulations and experiments indicate that these algorithms lead to efficient planning of time-optimal and smooth trajectory in joint space.

关键词： Trajectory planning Serial kinematic manipulator Quintic B-spline adaptive cuckoo search algorithm

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adaptive cuckoo search algorithm for Short-Term Fixed-Head Hydrothermal Scheduling Problem with Reservoir Volume Constraints

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INTERNATIONAL JOURNAL OF GRID AND DISTRIBUTED COMPUTING 2016年第5期9卷 191-204页

作者： Bach Hoang Dinh Thang Trung Nguyen Dieu Ngoc Vo Ton Duc Thang Univ Fac Elect & Elect Engn Tan Phong Ward 19 Nguyen Huu Tho StDist 7 Ho Chi Minh City Vietnam HCM City Univ Technol Dept Power Syst 268 Ly Thuong Kiet Str10th Dist Ho Chi Minh City Vietnam

This paper present three versions of cuckoo search algorithm (CSA) including conventional cuckoo search algorithm (CSA), modified CSA (MCSA) and adaptive CSA (ACSA) for solving the fixed head short-term hydrothermal scheduling (ST-HTS) problem where the reservoir volume constraints and nonconvex fuel cost function of thermal unit as well as the power losses in transmission line are taken into account. Among the applied methods, ACSA is first developed in the study by performing two modifications on second new solution generation via the action of an alien egg to be abandoned. In the ACSA, all initial solutions or all solutions at the end of the previous iteration are evaluated and sorted into two kinds of solution, good solutions with lower fitness function and bad solutions with higher fitness function. The implementation of the first new solution generation first via Levy flights in the ACSA is carried out similarly to that in MCSA. However, at the second new solution generation the ACSA evaluates the current solutions to choose the best one and use the information of the best one with a random solution to generate the second new solutions via the action of an alien egg to be abandoned. In addition, the probability of an alien egg discovery is considered an adaptive variable, which is set to the largest value at the beginning and decreased as the iteration is increased. Due to the adaptive value of the parameter, the ACSA can search an optimal solution but the trial runs are significantly decreased compared to CSA and MCSA. The performance of the ACSA is validated by testing on two systems and comparing with CSA, MCSA and other existing methods available in the paper.

关键词： adaptive cuckoo search algorithm reservoir volume constraint fixed head hydrothermal scheduling

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Design Optimization of Structural Engineering Problems Using adaptive cuckoo search algorithm 3

Design Optimization of Structural Engineering Problems Using...

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3rd IEEE International Conference on Control, Automation and Robotics (ICCAR)

作者： Pauline, Ong Sin, Ho Choon Sheng, Desmond Daniel Chin Vui Kiong, Sia Chee Meng, Ong Kok Univ Tun Hussein Onn Malaysia Fac Mech & Mfg Engn Batu Pahat Malaysia

ISBN: (纸本)9781509060887

This paper proposes an adaptive cuckoo search algorithm (ACSA) for optimization of structural engineering problems. ACSA - an improved cuckoo search algorithm, utilizes an adaptive step size selection strategy its diversification process. This approach improves the convergence characteristic while preserves the balance between intensification and diversification performances in the CSA simultaneously. The effectiveness of the ACSA in solving structural optimization problems is demonstrated in three structural engineering problems. Performance assessment shows that the ACSA outperforms the standard CSA and other methods available in the literature in most of the case studies.

关键词： adaptive cuckoo search algorithm cuckoo search algorithm structural engineering optimization

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An Improved Three-dimensional DV-Hop Localization algorithm Optimized by adaptive cuckoo search algorithm

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INTERNATIONAL JOURNAL OF ONLINE ENGINEERING 2017年第2期13卷 102-118页

作者： Zhang, Lieping Peng, Fei Cao, Peng Ji, Wenjun Guilin Univ Technol Coll Mech & Control Engn Guilin Peoples R China Guilin Univ Technol Coll Informat Sci & Engn Guilin Peoples R China

Aiming at the low accuracy of DV-Hop localization algorithm in three-dimensional localization of wireless sensor network, a DV-Hop localization algorithm optimized by adaptive cuckoo search algorithm was proposed in this paper. Firstly, an improved DV-Hop algorithm was proposed, which can reduce the localization error of DV-Hop algorithm by controlling the network topology and improving the method for calculating average hop distance. Meanwhile, aiming at the slow convergence in traditional cuckoo search algorithm, the adaptive strategy was improved for the step search strategy and the bird's nest recycling strategy. And the adaptive cuckoo search algorithm was introduced to the process of node localization to optimize the unknown node position estimation. The experiment results show that compared with the improved DV-Hop algorithm and the traditional DV-Hop algorithm, the DV-Hop algorithm optimized by adaptive cuckoo search algorithm improved the localization accuracy and reduced the localization errors.

关键词： WSN three-dimensional localization DV-Hop algorithm adaptive cuckoo search algorithm

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Numerical design optimization of real size complex steel space frame structures using a novel adaptive cuckoo search method

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INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN ENGINEERING 2024年第4期125卷 e7386-e7386页

作者： Carbas, Serdar Tunca, Osman Karamanoglu Mehmetbey Univ Dept Civil Engn Karaman Turkiye

In this study, real size complex steel space frame structures are numerically designed to achieve optimal design weight. For this aim, standard cuckoo search algorithm is rectified through a newly proposed adaptive method over two fundamental algorithmic parameters of alien egg probability detection (Pa) and step size control (alpha) to overcome incompetency of trapping into local optima. Besides, to strengthen exploitation phase of newly proposed algorithm, it is boosted with greedy selection (GS). So, novel algorithm has more promising exploration and exploitation capabilities. An 8-story, 1024-member and a 20-story, 1860-member real size complex steel space frame structures are selected as design examples. Also, initially to verify the supremacy of novel algorithm, a well-known welded beam is optimized as a benchmark structural design problem. Afterwards, the steel space frame structures are optimally designed via novel algorithm. Since ready steel section lists are utilized as selection pool for design variables, discrete programming problem is come into existence. The dead, live, snow, and wind design loads acting on frame structures are calculated in direction of ASCE 7-05 provisions. Furthermore, the structural design constraints are determined from LRFD-AISC specifications. Eventually, the newly proposed adaptive cuckoo search algorithm boosted with GS presents outstanding algorithmic performance.

关键词： adaptive cuckoo search algorithm greedy selection metaheuristics real size steel frames structural design optimization

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A Novel Maximum Power Point Tracking Technique for Photovoltaic Systems Operating Under Partial Shading Conditions 3

A Novel Maximum Power Point Tracking Technique for Photovolt...

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3rd International Conference on New Energy and Power Engineering, ICNEPE 2023

作者： Li, Lin Wang, Hao Li, Chongbing Wang, Shenping Zhao, Weiwei Prinx Chengshan Shandong Rongcheng264300 China Wuhan University of Technology School of Mechanical and Electronic Engineering Hubei Wuhan430070 China University of Birmingham School of Chemical Engineering BirminghamB15 2TT United Kingdom

ISBN: (纸本)9798350393224

Optimizing the electrical operating point is crucial for photovoltaic (PV) systems due to the significant impact of environmental factors on their output power. Conventional maximum power point tracking (MPPT) algorithms often face challenges with local maximum (LM) traps when partial shading conditions (PSCs) are present. This paper introduces a novel MPPT algorithm, named adaptive cuckoo search algorithm combined with Perturbation Observation (ACSA&PO), specifically designed for PV systems operating under rapidly changing solar irradiance and PSCs. The CSA component of the algorithm is enhanced through multiple dynamic strategies to improve convergence towards the global maximum (GM). Subsequently, the PO method is employed with a small step size to rapidly converge to the GM. Several test cases are devised to evaluate the performance of the MPPT algorithm utilizing ACSA&PO. Comparative analysis with efficient metaheuristic algorithms, including PO, CSA, Particle Swarm Optimization (PSO), and Grey Wolf Optimization (GWO), confirms that the proposed algorithm exhibits notable characteristics such as shortened convergence time and high tracking efficiency across diverse weather conditions. In various test scenarios, the ACSA&PO-based algorithm achieves an additional energy extraction of 1.67%, with an average tracking efficiency of 99.818%, leading to a 43.9% improvement in tracking time. © 2023 IEEE.

关键词： adaptive cuckoo search algorithm Maximum power point tracking partial shading conditions Perturbation Observation Method

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Applied of ACS Meta-heuristic algorithm and PSO Heuristic algorithm in Ultrasonic Signals 3

Applied of ACS Meta-heuristic Algorithm and PSO Heuristic Al...

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3rd International Conference on Intelligent Computing in Data Sciences (ICDS)

作者： Chibane, Farid Benammar, Abdessalem Drai, Redouane Res Ctr Ind Technol CRTI POB 64 Algiers 16014 Algeria Univ Boumerdes Lab Electrificat Entreprises Ind Boumerdes Algeria

ISBN: (纸本)9781728100036

The accurate estimation of the ultrasonic backscattered echoes pattern is essential in ultrasonic non-destructive evaluation. In this paper, a generalized parametric ultrasonic echo mode was presented. It is influenced by a set of parameters: echo bandwidth, arrival time, center frequency, amplitude and phase. The adaptive cuckoo search (ACS) and Particle Swarm Optimization (PSO) algorithms are used to estimate these parameters and there performances are compared. In first, simulations are carried out to assess the performance of the two algorithms, then these algorithms were applied on experimental ultrasonic signal. The ACS algorithm outperforms PSO.

关键词： Ultrasonic Signal Echo Parameter Estimation adaptive cuckoo search algorithm Particle Swarm Optimization

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A novel method based on adaptive cuckoo search for optimal network reconfiguration and distributed generation allocation in distribution network

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INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2016年 78卷 801-815页

作者： Thuan Thanh Nguyen Anh Viet Truong Tuan Anh Phung HCMC Univ Technol & Educ Fac Elect & Elect Engn 1 Vo Van Ngan Str Ho Chi Minh City Vietnam Dong An Polytech 30-4 Str Di An Dist Binh Duong Prov Vietnam Ha Noi Univ Technol 1 Dai Co Viet Str Hanoi Vietnam

This paper proposes a new methodology to optimize network topology and placement of distributed generation (DG) in distribution network with an objective of reduction real power loss and voltage stability enhancement. A meta-heuristic cuckoo search algorithm (CSA) inspired from the obligate brood parasitism of some cuckoo species which lay their eggs in the nests of other birds of other species for solving optimization problems is adapted to simultaneously reconfigure and identify the optimal location and size of DG units in a distribution network. The graph theory is used to determine the search space which reduces infeasible network configurations of reconfiguration process and check the radial constraint of each configuration of distribution network. The effectiveness of the proposed method has been validated on three different distribution network systems at seven different scenarios. The obtained results show well the effectiveness and performance of the proposed method in distribution network reconfiguration with optimal location and size of DG problems. (C) 2015 Elsevier Ltd. All rights reserved.

关键词： Distribution network reconfiguration Distributed generator adaptive cuckoo search algorithm Power loss reduction Voltage stability enhancement

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