Beetle swarm optimisation for solving investment portfolio problems

Chen, Tingting; Zhu, Yu; Teng, Jun

doi:10.1049/joe.2018.8287

Cited by 38 publications

(21 citation statements)

References 20 publications

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“…e BAS algorithm can fit well for the real-time task. e performance of beetle antennae search algorithm relies heavily on the initial position, and it will easily fall into the local optimum when dealing with high-dimensional problems [37][38][39]. In order to overcome these drawbacks, further improvements are made by combining the antennae search algorithm and particle swarm optimization.…”

Section: Beetle Antennae Search Algorithm (Bas)mentioning

confidence: 99%

Improvement of Heavy Load Robot Positioning Accuracy by Combining a Model-Based Identification for Geometric Parameters and an Optimized Neural Network for the Compensation of Nongeometric Errors

Wang

Chen

et al. 2020

Complexity

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The positioning accuracy of a robot is of great significance in advanced robotic manufacturing systems. This paper proposes a novel calibration method for improving robot positioning accuracy. First of all, geometric parameters are identified on the basis of the product of exponentials (POE) formula. The errors of the reduction ratio and the coupling ratio are identified at the same time. Then, joint stiffness identification is carried out by adding a load to the end-effector. Finally, residual errors caused by nongeometric parameters are compensated by a multilayer perceptron neural network (MLPNN) based on beetle swarm optimization algorithm. The calibration is implemented on a SIASUN SR210D robot manipulator. Results show that the proposed method possesses better performance in terms of faster convergence and higher precision.

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Section: Beetle Antennae Search Algorithm (Bas)mentioning

confidence: 99%

Improvement of Heavy Load Robot Positioning Accuracy by Combining a Model-Based Identification for Geometric Parameters and an Optimized Neural Network for the Compensation of Nongeometric Errors

Wang

Chen

et al. 2020

Complexity

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“…where move_x1 denotes the beetle moving distance along the horizontal direction, and move_x2 denotes the beetle moving distance along the vertical direction. According to (16), (17), (18) and (19), the position updates for the beetle are implemented in Verilog HDL as follows:…”

Section: Updating the Beetle Position In Terms Of Verilog Hdl Implmentioning

confidence: 99%

“…They have similar struct according to Figure 6 and Figure 7. The only difference between them is the direction operation based on (16) and (17).…”

Section: The Antennae Modulesmentioning

confidence: 99%

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A Hardware Descriptive Approach to Beetle Antennae Search

Yue

Jiang

et al. 2020

IEEE Access

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Beetle antennae search (BAS) is a newly developed meta-heuristic algorithm which is effectively used for optimizing objective functions of complex forms or even unknown forms. The common practice for implementing meta-heuristic algorithms including the BAS largely relies on programming in a high-level language and executing the code on a computer platform. However, the high-level implementation of the BAS algorithm hinders it from being used in an embedding system, where real-time operations are normally required. To address this limitation, we present an approach to implementing the BAS algorithm on a field-programmable gate array (FPGA). Specifically, we program the BAS function in the Verilog hardware description language (HDL), which provides a tractable vehicle for implementing the BAS algorithm at the gate level on the FPGA chip. We simulate our Verilog HDL based BAS module with the Modelsim platform. Simulation results validate the feasibility of our proposed Verilog HDL implementation of the BAS. Additionally, we implement the BAS model on the Zynq XC7Z010 platform, with 132.5 µ s latency for model implementation. INDEX TERMS FPGA, beetle antennae search, Verilog HDL, optimization algorithm.

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“…The beetle antennae search (BAS) algorithm is a new type of computational intelligence algorithms which is applied in scientific and engineering applications, e.g., geomechanics analysis [22], network training [23], controller parameter tuning in servo system [24], unmanned aerial vehicle path planning [25], route planning [26], pattern classification [23], power system [27], ship predictive collision avoidance [28], bridge sensor placement [29], and investment portfolio problems [30]. Because of the excellent nonlinear optimization ability, BAS can be regarded as a potential efficient optimizer in robot applications.…”

Section: Introductionmentioning

confidence: 99%

Motion Planning of Redundant Manipulator With Variable Joint Velocity Limit Based on Beetle Antennae Search Algorithm

Cheng

et al. 2020

IEEE Access

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Redundant manipulators play important roles in many industrial and service applications by assisting people fulfill heavy and repetitive jobs. However, redundant manipulators are coupled highlynonlinear systems which exert difficulty of redundancy resolution computation. Conventional methods such as pseudo-inverse-based approaches obtain the resolved joint angles from joint velocity level, which may bring about more computational cost and may neglect joint velocity limits. In this work, a motion planning method based on beetle antennae search algorithm (BAS) is proposed for motion planning of redundant manipulators with the variable joint velocity limit. Such proposed work does not need to resolve the velocity kinematics equation as the conventional methods do, and the proposed method can directly deal with the forward kinematics equation to resolve the desired joint angles. The simulation and experiment on the five-link planar manipulator and the Kuka industrial manipulator system demonstrate the efficiency of the proposed method for motion planning of redundant manipulator, and reveal the reliable performance of the BAS algorithm as compared with genetic algorithm (GA), particle swarm optimization (PSO), firefly algorithm(FA) and quantum behaved particle swarm algorithm(QPSO) methods.

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Beetle swarm optimisation for solving investment portfolio problems

Cited by 38 publications

References 20 publications

Improvement of Heavy Load Robot Positioning Accuracy by Combining a Model-Based Identification for Geometric Parameters and an Optimized Neural Network for the Compensation of Nongeometric Errors

Improvement of Heavy Load Robot Positioning Accuracy by Combining a Model-Based Identification for Geometric Parameters and an Optimized Neural Network for the Compensation of Nongeometric Errors

A Hardware Descriptive Approach to Beetle Antennae Search

Motion Planning of Redundant Manipulator With Variable Joint Velocity Limit Based on Beetle Antennae Search Algorithm

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