Accuracy improvement of 6-UPS Stewart forward kinematics solution based on self-aggregating MFO algorithm

2021 ◽  
Vol 40 (5) ◽  
pp. 8831-8846
Author(s):  
Wanzheng Liu ◽  
Ying Xu ◽  
Meng Shao ◽  
Guodong Yue ◽  
Dong An
Keyword(s):  
Positive Solution ◽  
Optimization Model ◽  
Optimization Problem ◽  
Heuristic Algorithms ◽  
Forward Kinematics ◽  
Local Optimum ◽  
Solution Quality ◽  
Moth Flame Optimization Algorithm ◽  
Global And Local ◽  
Better Than

In this paper, a Stewart’s positive solution optimization model is proposed, for obtaining the complex solution to a Stewart’s forward kinematics problem, considering the existence of multiple solutions. The model converts the positive kinematics problem into an optimization problem, in which the value of the objective function is used to represent the precision of Stewart’s positive solution. A self-aggregating moth–flame optimization algorithm (SMFO) is used to improve the accuracy of Stewart’s forward kinematics solution. Two features were added to the conventional MFO algorithm to obtain a more stable balance between global and local explorations. First, Gaussian distribution was used for the flame population to select suitable individuals for Levy Flight operation, increase the diversity of the population, and enhance the algorithm’s ability to jump out of a local optimum. Second, in the middle and late iterations, the positions of the flames were periodically adjusted using the light intensity-attraction characteristic (LIAC) to strengthen the connection between individual flames and enhance the local exploration ability of the algorithm. The proposed SMFO algorithm is compared with three classic meta-heuristic algorithms for eight benchmark functions. Experimental results indicate that the SMFO algorithm is significantly better than the other three algorithms in terms of solution quality and convergence rate. To verify the effectiveness of the SMFO algorithm in solving the Stewart positive kinematics optimization model, values of eight sets of conventional position and posture parameters as well as limiting position and posture parameters were randomly obtained, and values of 16 sets of position and posture parameters were obtained using four algorithms. The results indicate that the SMFO algorithm can improve the accuracy of the forward kinematics solution to 4.05E-09 mm.

scholarly journals Adaptive Operator Quantum-Behaved Pigeon-Inspired Optimization Algorithm with Application to UAV Path Planning

Algorithms ◽  
2018 ◽  
Vol 12 (1) ◽  
pp. 3 ◽  
Author(s):  
Chunhe Hu ◽  
Yu Xia ◽  
Junguo Zhang
Keyword(s):  
Path Planning ◽  
Nonlinear Problems ◽  
Mapping Method ◽  
Initial Population ◽  
Local Optimum ◽  
Planning Problem ◽  
Dynamic Constraints ◽  
Logistic Mapping ◽  
Global And Local ◽  
Better Than

Path planning of unmanned aerial vehicles (UAVs) in threatening and adversarial areas is a constrained nonlinear optimal problem which takes a great amount of static and dynamic constraints into account. Quantum-behaved pigeon-inspired optimization (QPIO) has been widely applied to such nonlinear problems. However, conventional QPIO is suffering low global convergence speed and local optimum. In order to solve the above problems, an improved QPIO algorithm, adaptive operator QPIO, is proposed in this paper. Firstly, a new initialization process based on logistic mapping method is introduced to generate the initial population of the pigeon-swarm. After that, to improve the performance of the map and compass operation, the factor parameter will be adaptively updated in each iteration, which can balance the ability between global and local search. In the final landmark operation, the gradual decreasing pigeon population-updating strategy is introduced to prevent premature convergence and local optimum. Finally, the demonstration of the proposed algorithm on UAV path planning problem is presented, and the comparison result indicates that the performance of our algorithm is better than that of particle swarm optimization (PSO), pigeon-inspired optimization (PIO), and its variants, in terms of convergence and accuracy.

SOSPD Controllers Tuning by Means of an Evolutionary Algorithm

2014 ◽  
Vol 4 (2) ◽  
pp. 40-58 ◽  
Author(s):  
Jesús-Antonio Hernández-Riveros ◽  
Jorge-Humberto Urrea-Quintero
Keyword(s):  
Evolutionary Algorithm ◽  
Optimization Problem ◽  
Heuristic Algorithms ◽  
Operating Mode ◽  
Performance Criterion ◽  
Order System ◽  
Optimal Tuning ◽  
Current Trends ◽  
Controlling Processes ◽  
Better Than

The Proportional Integral Derivative (PID) controller is the most widely used industrial device to monitoring and controlling processes. There are numerous methods for estimating the controller parameters, in general, resolving particular cases. Current trends in parameter estimation minimize an integral performance criterion. Therefore, the calculation of the controller parameters is proposed as an optimization problem. Although there are alternatives to the traditional rules of tuning, there is not yet a study showing that the use of heuristic algorithms it is indeed better than using the classic methods of optimal tuning. In this paper, the evolutionary algorithm MAGO is used as a tool to optimize the controller parameters. The procedure is applied to a range of standard plants modeled as a Second Order System plus Time Delay. Better results than traditional methods of optimal tuning, regardless of the operating mode of the controller, are yielded.

scholarly journals Spiral Motion Enhanced Elite Whale Optimizer for Global Tasks

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-33
Author(s):  
GuoChun Wang ◽  
Wenyong Gui ◽  
Guoxi Liang ◽  
Xuehua Zhao ◽  
Mingjing Wang ◽  
...  
Keyword(s):  
High Performance ◽  
Local Optimum ◽  
Solution Quality ◽  
Original Algorithm ◽  
Spiral Motion ◽  
Elite Strategy ◽  
Whale Optimization ◽  
Engineering Problems ◽  
Practical Engineering ◽  
Better Than

The whale optimization algorithm (WOA) is a high-performance metaheuristic algorithm that can effectively solve many practical problems and broad application prospects. However, the original algorithm has a significant improvement in space in solving speed and precision. It is easy to fall into local optimization when facing complex or high-dimensional problems. To solve these shortcomings, an elite strategy and spiral motion from moth flame optimization are utilized to enhance the original algorithm’s efficiency, called MEWOA. Using these two methods to build a more superior population, MEWOA further balances the exploration and exploitation phases and makes it easier for the algorithm to get rid of the local optimum. To show the proposed method’s performance, MEWOA is contrasted to other superior algorithms on a series of comprehensive benchmark functions and applied to practical engineering problems. The experimental data reveal that the MEWOA is better than the contrast algorithms in convergence speed and solution quality. Hence, it can be concluded that MEWOA has great potential in global optimization.

scholarly journals NEW HEURISTIC ALGORITHMS FOR ROLLING AIR FRAME AERODYNAMIC PARAMETERS ESTIMATION

Aviation ◽  
2020 ◽  
Vol 24 (1) ◽  
pp. 20-32 ◽  
Author(s):  
Ayham Mohamad ◽  
Jalal Karimi ◽  
Alireza Naderi
Keyword(s):  
Optimization Problem ◽  
Particle Swarm Optimization Algorithm ◽  
Heuristic Algorithms ◽  
Optimization Problems ◽  
Parameters Estimation ◽  
Modified Particle Swarm Optimization ◽  
Smoothing Filter ◽  
Aerodynamic Parameters ◽  
New Strategies ◽  
Better Than

In this research, based on heuristic optimization algorithms, three new strategies are developed for Aerodynamic Parameters Estimation (APE) of one pair ON-OFF actuator rolling airframe. In the 1st method namely EAM-PSO the aerodynamic parameters are directly estimated. While, the next two algorithms called EBM-PSO and SEBM-PSO are two-step strategies. In the 1st step the aerodynamic forces and moments are estimated, then after passing through a designed smoothing filter, in the 2nd step aerodynamic parameters are estimated. In EBM-PSO all the aerodynamic parameters are estimated at once by solving one optimization problem. In SEBM-PSO the APE is converted to solve four separate optimization problems. A modified particle swarm optimization algorithm is developed and used in estimation process. The performance of proposed algorithms is compared with that of state of the art algorithm EKF. The simulation results show that SEBM-PSO and EBM-PSO are better than EAM-PSO in term of accuracy and run time.

Quantum-Behaved Bat Algorithm for Solving the Economic Load Dispatch Problem Considering a Valve-Point Effect

2020 ◽  
Vol 11 (3) ◽  
pp. 41-57
Author(s):  
Pandian Vasant ◽  
Fahad Parvez Mahdi ◽  
Jose Antonio Marmolejo-Saucedo ◽  
Igor Litvinchev ◽  
Roman Rodriguez Aguilar ◽  
...  
Keyword(s):  
Nonlinear Optimization ◽  
Optimization Problem ◽  
Optimization Problems ◽  
Bat Algorithm ◽  
Economic Load Dispatch ◽  
Solution Quality ◽  
Real World Problem ◽  
Valve Point Effect ◽  
Power Generation Systems ◽  
Better Than

Quantum computing-inspired metaheuristic algorithms have emerged as a powerful computational tool to solve nonlinear optimization problems. In this paper, a quantum-behaved bat algorithm (QBA) is implemented to solve a nonlinear economic load dispatch (ELD) problem. The objective of ELD is to find an optimal combination of power generating units in order to minimize total fuel cost of the system, while satisfying all other constraints. To make the system more applicable to the real-world problem, a valve-point effect is considered here with the ELD problem. QBA is applied in 3-unit, 10-unit, and 40-unit power generation systems for different load demands. The obtained result is then presented and compared with some well-known methods from the literature such as different versions of evolutionary programming (EP) and particle swarm optimization (PSO), genetic algorithm (GA), differential evolution (DE), simulated annealing (SA) and hybrid ABC_PSO. The comparison of results shows that QBA performs better than the above-mentioned methods in terms of solution quality, convergence characteristics and computational efficiency. Thus, QBA proves to be an effective and a robust technique to solve such nonlinear optimization problem.

scholarly journals Automatic Design of Heuristic Algorithms for Binary Optimization Problems

2021 ◽  
Author(s):  
Marcelo de Souza
Keyword(s):  
Optimization Problem ◽  
Heuristic Algorithms ◽  
Optimization Problems ◽  
State Of The Art ◽  
Algorithm Design ◽  
Automatic Design ◽  
Binary Optimization ◽  
Automatic Algorithm ◽  
Better Than ◽  
Design Techniques

In this work we present AutoBQP, a heuristic solver for binary optimization problems. It applies automatic algorithm design techniques to search for the best heuristics for a given optimization problem. Experiments show that the solver can find algorithms which perform better than or comparable to state-of-the-art methods, and can even find new best solutions for some instances of standard benchmark sets.

scholarly journals Multi-user Remote Lab: Timetable Scheduling Using Simplex Nondominated Sorting Genetic Algorithm

2021 ◽  
Vol 2 (2) ◽  
pp. 1-13
Author(s):  
Seid Miad Zandavi ◽  
Vera Chung ◽  
Ali Anaissi
Keyword(s):  
Genetic Algorithm ◽  
Optimization Algorithm ◽  
Heuristic Algorithms ◽  
Simplex Algorithm ◽  
Local Optimum ◽  
Hybrid Optimization Algorithm ◽  
Multimodal Function ◽  
Remote Laboratories ◽  
Remote Lab ◽  
Timetable Problem

The scheduling of multi-user remote laboratories is modeled as a multimodal function for the proposed optimization algorithm. The hybrid optimization algorithm, hybridization of the Nelder-Mead Simplex algorithm, and Non-dominated Sorting Genetic Algorithm (NSGA), named Simplex Non-dominated Sorting Genetic Algorithm (SNSGA), is proposed to optimize the timetable problem for the remote laboratories to coordinate shared access. The proposed algorithm utilizes the Simplex algorithm in terms of exploration and NSGA for sorting local optimum points with consideration of potential areas. SNSGA is applied to difficult nonlinear continuous multimodal functions, and its performance is compared with hybrid Simplex Particle Swarm Optimization, Simplex Genetic Algorithm, and other heuristic algorithms. The results show that SNSGA has a competitive performance to address timetable problems.

scholarly journals Secure the IoT Networks as Epidemic Containment Game

Symmetry ◽  
2021 ◽  
Vol 13 (2) ◽  
pp. 156
Author(s):  
Juntao Zhu ◽  
Hong Ding ◽  
Yuchen Tao ◽  
Zhen Wang ◽  
Lanping Yu
Keyword(s):  
Heuristic Algorithms ◽  
Heuristic Method ◽  
Exact Algorithms ◽  
Epidemic Threshold ◽  
Solution Quality ◽  
Sis Model ◽  
Linear Programming Formulation ◽  
Iot Devices ◽  
General Graphs ◽  
The Internet Of Things

The spread of a computer virus among the Internet of Things (IoT) devices can be modeled as an Epidemic Containment (EC) game, where each owner decides the strategy, e.g., installing anti-virus software, to maximize his utility against the susceptible-infected-susceptible (SIS) model of the epidemics on graphs. The EC game’s canonical solution concepts are the Minimum/Maximum Nash Equilibria (MinNE/MaxNE). However, computing the exact MinNE/MaxNE is NP-hard, and only several heuristic algorithms are proposed to approximate the MinNE/MaxNE. To calculate the exact MinNE/MaxNE, we provide a thorough analysis of some special graphs and propose scalable and exact algorithms for general graphs. Especially, our contributions are four-fold. First, we analytically give the MinNE/MaxNE for EC on special graphs based on spectral radius. Second, we provide an integer linear programming formulation (ILP) to determine MinNE/MaxNE for the general graphs with the small epidemic threshold. Third, we propose a branch-and-bound (BnB) framework to compute the exact MinNE/MaxNE in the general graphs with several heuristic methods to branch the variables. Fourth, we adopt NetShiled (NetS) method to approximate the MinNE to improve the scalability. Extensive experiments demonstrate that our BnB algorithm can outperform the naive enumeration method in scalability, and the NetS can improve the scalability significantly and outperform the previous heuristic method in solution quality.

Trajectory Optimization Applied to Space Rendezvous

2013 ◽  
Vol 756-759 ◽  
pp. 3466-3470
Author(s):  
Xu Min Song ◽  
Qi Lin
Keyword(s):  
Simulated Annealing ◽  
Optimization Model ◽  
Trajectory Optimization ◽  
Optimization Problem ◽  
Optimization Method ◽  
Nonlinear Constraints ◽  
Design Variables ◽  
Adaptive Simulated Annealing ◽  
Simulation Results

The trajcetory plan problem of spece reandezvous mission was studied in this paper using nolinear optimization method. The optimization model was built based on the Hills equations. And by analysis property of the design variables, a transform was put forward , which eliminated the equation and nonlinear constraints as well as decreaseing the problem dimensions. The optimization problem was solved using Adaptive Simulated Annealing (ASA) method, and the rendezvous trajectory was designed.The method was validated by simulation results.

An improved sparrow search algorithm based on levy flight and opposition-based learning

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Danni Chen ◽  
JianDong Zhao ◽  
Peng Huang ◽  
Xiongna Deng ◽  
Tingting Lu
Keyword(s):  
Parameter Optimization ◽  
Optimization Problem ◽  
Heuristic Algorithms ◽  
Search Algorithm ◽  
Convergence Speed ◽  
Support Vector ◽  
Lévy Flight ◽  
Levy Flight ◽  
Content Type ◽  
Opposition Based Learning

Purpose Sparrow search algorithm (SSA) is a novel global optimization method, but it is easy to fall into local optimization, which leads to its poor search accuracy and stability. The purpose of this study is to propose an improved SSA algorithm, called levy flight and opposition-based learning (LOSSA), based on LOSSA strategy. The LOSSA shows better search accuracy, faster convergence speed and stronger stability. Design/methodology/approach To further enhance the optimization performance of the algorithm, The Levy flight operation is introduced into the producers search process of the original SSA to enhance the ability of the algorithm to jump out of the local optimum. The opposition-based learning strategy generates better solutions for SSA, which is beneficial to accelerate the convergence speed of the algorithm. On the one hand, the performance of the LOSSA is evaluated by a set of numerical experiments based on classical benchmark functions. On the other hand, the hyper-parameter optimization problem of the Support Vector Machine (SVM) is also used to test the ability of LOSSA to solve practical problems. Findings First of all, the effectiveness of the two improved methods is verified by Wilcoxon signed rank test. Second, the statistical results of the numerical experiment show the significant improvement of the LOSSA compared with the original algorithm and other natural heuristic algorithms. Finally, the feasibility and effectiveness of the LOSSA in solving the hyper-parameter optimization problem of machine learning algorithms are demonstrated. Originality/value An improved SSA based on LOSSA is proposed in this paper. The experimental results show that the overall performance of the LOSSA is satisfactory. Compared with the SSA and other natural heuristic algorithms, the LOSSA shows better search accuracy, faster convergence speed and stronger stability. Moreover, the LOSSA also showed great optimization performance in the hyper-parameter optimization of the SVM model.

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