scholarly journals Optimization of CSO algorithm based on adaptive inertia weight coefficient

2021 ◽  
Vol 2078 (1) ◽  
pp. 012009
Author(s):  
Dongnan Suo
Keyword(s):  
Weight Coefficient ◽  
Local Extremum ◽  
Test Functions ◽  
Inertia Weight ◽  
Learning Factor ◽  
Fitness Value ◽  
Simulation Results ◽  
Crossover And Mutation ◽  
Speed And Accuracy ◽  
Typical Test

Abstract The traditional CSO algorithm is easy to fall into local extremum in optimization. In this paper, a CSO algorithm based on weight coefficient is proposed. In the CSO algorithm, the inertia weight coefficient is introduced into the hen position formula, and the learning factor influenced by the rooster is added to the chick position formula. Finally, using the idea of heredity, individuals with excellent fitness value are selected for crossover and mutation with a certain probability. Through the simulation comparison of five typical test functions, the simulation results show that the improved CSO algorithm can avoid local optimization, strengthen the global extreme value search ability, and improve the convergence speed and accuracy range of the algorithm.

Enhanced Artificial Intelligent Method for Representing the Photonic Crystal Fiber Profile Index

2018 ◽  
Vol 1 (1) ◽  
pp. 2-19
Author(s):  
Mahmood Sh. Majeed ◽  
Raid W. Daoud
Keyword(s):  
Genetic Algorithms ◽  
Photonic Crystal ◽  
Photonic Crystal Fiber ◽  
New Method ◽  
Proper Solution ◽  
Artificial Intelligent ◽  
Crystal Fiber ◽  
Fitness Value ◽  
First Pass ◽  
Crossover And Mutation

A new method proposed in this paper to compute the fitness in Genetic Algorithms (GAs). In this new method the number of regions, which assigned for the population, divides the time. The fitness computation here differ from the previous methods, by compute it for each portion of the population as first pass, then the second pass begin to compute the fitness for population that lye in the portion which have bigger fitness value. The crossover and mutation and other GAs operator will do its work only for biggest fitness portion of the population. In this method, we can get a suitable and accurate group of proper solution for indexed profile of the photonic crystal fiber (PCF).

scholarly journals Pulmonary Nodule Recognition Based on Multiple Kernel Learning Support Vector Machine-PSO

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Yang Li ◽  
Zhichuan Zhu ◽  
Alin Hou ◽  
Qingdong Zhao ◽  
Liwei Liu ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Pulmonary Nodule ◽  
Multiple Kernel Learning ◽  
Pso Algorithm ◽  
Support Vector ◽  
Training Time ◽  
Inertia Weight ◽  
Fitness Value ◽  
Svm Algorithm ◽  
Fitness Curve

Pulmonary nodule recognition is the core module of lung CAD. The Support Vector Machine (SVM) algorithm has been widely used in pulmonary nodule recognition, and the algorithm of Multiple Kernel Learning Support Vector Machine (MKL-SVM) has achieved good results therein. Based on grid search, however, the MKL-SVM algorithm needs long optimization time in course of parameter optimization; also its identification accuracy depends on the fineness of grid. In the paper, swarm intelligence is introduced and the Particle Swarm Optimization (PSO) is combined with MKL-SVM algorithm to be MKL-SVM-PSO algorithm so as to realize global optimization of parameters rapidly. In order to obtain the global optimal solution, different inertia weights such as constant inertia weight, linear inertia weight, and nonlinear inertia weight are applied to pulmonary nodules recognition. The experimental results show that the model training time of the proposed MKL-SVM-PSO algorithm is only 1/7 of the training time of the MKL-SVM grid search algorithm, achieving better recognition effect. Moreover, Euclidean norm of normalized error vector is proposed to measure the proximity between the average fitness curve and the optimal fitness curve after convergence. Through statistical analysis of the average of 20 times operation results with different inertial weights, it can be seen that the dynamic inertial weight is superior to the constant inertia weight in the MKL-SVM-PSO algorithm. In the dynamic inertial weight algorithm, the parameter optimization time of nonlinear inertia weight is shorter; the average fitness value after convergence is much closer to the optimal fitness value, which is better than the linear inertial weight. Besides, a better nonlinear inertial weight is verified.

scholarly journals Glowworm Swarm Optimization and Its Application to Blind Signal Separation

2016 ◽  
Vol 2016 ◽  
pp. 1-8 ◽  
Author(s):  
Zhucheng Li ◽  
Xianglin Huang
Keyword(s):  
Objective Function ◽  
Global Optimum ◽  
Convergence Speed ◽  
Signal Separation ◽  
Blind Signal Separation ◽  
Swarm Optimization ◽  
Blind Signal ◽  
Glowworm Swarm Optimization ◽  
Simulation Results ◽  
Speed And Accuracy

Traditional optimization algorithms for blind signal separation (BSS) are mainly based on the gradient, which requires the objective function to be continuous and differentiable, so the applications of these algorithms are very limited. Moreover, these algorithms have problems with the convergence speed and accuracy. To overcome these drawbacks, this paper presents a modified glowworm swarm optimization (MGSO) algorithm based on a novel step adjustment rule and then applies MGSO to BSS. Taking kurtosis of the mixed signals as the objective function of BSS, MGSO-BSS succeeds in separating the mixed signals in Matlab environment. The simulation results prove that MGSO is more effective in capturing the global optimum of the objective function of the BSS algorithm and has faster convergence speed and higher accuracy, compared with particle swarm optimization (PSO) and GSO.

scholarly journals Fuzzy adaptive catfish particle swarm optimization

2012 ◽  
Vol 1 (2) ◽  
pp. 149 ◽  
Author(s):  
Li-Yeh Chuang ◽  
Sheng-Wei Tsai ◽  
Cheng-Hong Yang
Keyword(s):  
Particle Swarm Optimization ◽  
Statistical Analysis ◽  
Particle Swarm ◽  
Test Functions ◽  
Swarm Optimization ◽  
Inertia Weight ◽  
Interactive Behavior ◽  
Different Dimensions ◽  
Swarm Intelligence Optimization ◽  
Fuzzy Adaptive

The catfish particle swarm optimization (CatfishPSO) algorithm is a novel swarm intelligence optimization technique.This algorithm was inspired by the interactive behavior of sardines and catfish. The observed catfish effect is applied toimprove the performance of particle swarm optimization (PSO). In this paper, we propose fuzzy CatfishPSO(F-CatfishPSO), which uses fuzzy to dynamically change the inertia weight of CatfishPSO. Ten benchmark functions with10, 20, and 30 different dimensions were selected as the test functions. Statistical analysis of the experimental resultsindicates that F-CatfishPSO outperformed PSO, F-PSO and CatfishPSO.

An Improved Particle Swarm Optimization Algorithm with Adaptive Inertia Weights

2019 ◽  
Vol 18 (03) ◽  
pp. 833-866 ◽  
Author(s):  
Mi Li ◽  
Huan Chen ◽  
Xiaodong Wang ◽  
Ning Zhong ◽  
Shengfu Lu
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Local Optimum ◽  
Random Mutation ◽  
Swarm Optimization ◽  
Inertia Weight ◽  
Fitness Value ◽  
The One ◽  
Individual Particles

The particle swarm optimization (PSO) algorithm is simple to implement and converges quickly, but it easily falls into a local optimum; on the one hand, it lacks the ability to balance global exploration and local exploitation of the population, and on the other hand, the population lacks diversity. To solve these problems, this paper proposes an improved adaptive inertia weight particle swarm optimization (AIWPSO) algorithm. The AIWPSO algorithm includes two strategies: (1) An inertia weight adjustment method based on the optimal fitness value of individual particles is proposed, so that different particles have different inertia weights. This method increases the diversity of inertia weights and is conducive to balancing the capabilities of global exploration and local exploitation. (2) A mutation threshold is used to determine which particles need to be mutated. This method compensates for the inaccuracy of random mutation, effectively increasing the diversity of the population. To evaluate the performance of the proposed AIWPSO algorithm, benchmark functions are used for testing. The results show that AIWPSO achieves satisfactory results compared with those of other PSO algorithms. This outcome shows that the AIWPSO algorithm is conducive to balancing the abilities of the global exploration and local exploitation of the population, while increasing the diversity of the population, thereby significantly improving the optimization ability of the PSO algorithm.

Optimal Operation of Multireservoir Systems by Enhanced Water Cycle Algorithm

2019 ◽  
Vol 7 (1) ◽  
pp. 27-43
Author(s):  
Yanjun Kong ◽  
Yadong Mei ◽  
Weinan Li ◽  
Ben Yue ◽  
Xianxun Wang
Keyword(s):  
Water Cycle ◽  
Water System ◽  
System Size ◽  
Optimal Operation ◽  
Test Functions ◽  
Water Cycle Algorithm ◽  
Adaptive Parameter ◽  
Fitness Value ◽  
Better Than

In this article, an enhanced water cycle algorithm (EWCA) is proposed and applied to optimize the operation of multireservoir systems. Three improvements have been made to the water cycle algorithm (WCA). They refer to high-quality initial solutions obtained by the chaos-based method, balancing of exploration of streams using a dynamic adaptive parameter, and dynamic variation of sub-water system size using the fitness value of rivers. For the purpose of verifying the improvements, three typical benchmark functions were selected as test functions. It has shown that EWCA performs better than WCA and water cycle algorithm with evaporation rate (ER-WCA). And then these three algorithms were also applied to optimize the operation of a multireservoir system with complex constrains as the case study. By comparing the results, it is found that the EWCA has higher ability to find a feasible solution in a narrow searching space. The effectiveness of the improvements is confirmed.

The Design of Adaptive Immune Vaccine Algorithm

2011 ◽  
Vol 308-310 ◽  
pp. 1094-1098 ◽  
Author(s):  
Gui Li Yuan ◽  
Yan Guang Xue ◽  
Qing Jiao Liang
Keyword(s):  
System Theory ◽  
Memory Cell ◽  
Immune Memory ◽  
Search Efficiency ◽  
Mutation Operator ◽  
Test Functions ◽  
Adaptive Immune ◽  
Crossover And Mutation ◽  
Convergence Stability

Aiming at disadvantages of Genetic Algorithm (GA) and learning from the immune system theory, this paper introduces immune memory cell of immune theory, vaccine extraction and vaccination operator based on immune theory, and adaptive probability crossover and mutation operator to GA, to improve the optimization ability and search efficiency of GA, and proposes Adaptive Immune Vaccine Algorithm (AIVA). Then proves the convergence of the algorithm, gives the composition mechanisms of the key operators, and verifies the role of each operator. Finally, four test functions have been optimized using GA, AIGA and AIVA. The experimental results show that AIVA effectively overcomes the GA Defects, greatly prevents the degradation of population, and has perfect convergence stability and excellent global optimization capability.

Dynamic Simulation of a Leadscrew Driven Flexible Robot Arm and Controller

1986 ◽  
Vol 108 (2) ◽  
pp. 119-126 ◽  
Author(s):  
Nabil G. Chalhoub ◽  
A. Galip Ulsoy
Keyword(s):  
High Performance ◽  
Industrial Robots ◽  
Body Motion ◽  
Structural Flexibility ◽  
Robot Arm ◽  
Flexible Robot ◽  
Spherical Coordinate ◽  
Performance Requirements ◽  
Simulation Results ◽  
Speed And Accuracy

High performance requirements in robotics have led to the consideration of structural flexibilty in robot arms. This paper employs an assumed modes method to model the flexible motion of the last link of a spherical coordinate robot arm. The model, which includes the non backdrivability of the leadscrews, is used to investigate relationships between the arm structural flexibility and a linear controller for the rigid body motion. This simple controller is used to simulate the controllers currently used in industrial robots. The simulation results illustrate the differences between leadscrew driven and unconstrained axes of the robot; they indicate the trade-off between speed and accuracy; and show potential instability mechanisms due to the interaction between the controller and the robot structural flexibility.

scholarly journals Antlion Algorithm Based on Immune Cloning and Its Truss Optimization

2021 ◽  
Author(s):  
Liyancang Li ◽  
Wuyue Yue Wu
Keyword(s):  
Optimization Algorithm ◽  
Learning Strategy ◽  
Optimization Problems ◽  
Early Stage ◽  
Truss Optimization ◽  
Test Functions ◽  
Combinatorial Optimization Problems ◽  
Fitness Value ◽  
Cauchy Mutation ◽  
Antlion Optimization

Abstract Antlion optimization algorithm has good search and development capabilities, but the influence weight of elite ant lions is reduced in the later stage of optimization, which leads to slower algorithm convergence and easy to fall into local optimization. For this purpose, an antlion optimization algorithm based on immune cloning was proposed. In the early stage, the reverse learning strategy was used to initialize the ant population. The Cauchy mutation operator was added to the elite antlion update to improve the later development ability of the algorithm; finally, the antlion was cloned and mutated with the immune clone selection algorithm to change the position and fitness value of the antlion, and further improve the algorithm's global optimization ability and convergence accuracy. 10 test functions and a 0~1 backpack were used to evaluate the optimization ability of the algorithm and applied to the size and layout optimization problems of the truss structure. The optimization effect was found to be good through the force effect diagram. It is verified that ICALO is applied to combinatorial optimization problems with faster convergence speed and higher accuracy. It provides a new method for structural optimization.This article is submitted as original content. The authors declare that they have no competing interests.

scholarly journals Solving Approach of Inverse Kinematics for Manipulators Based on Improved Adaptive Niche Genetic Algorithm

2019 ◽  
Vol 37 (3) ◽  
pp. 488-495
Author(s):  
Huizhen Yang ◽  
Xiyang Liu
Keyword(s):  
Genetic Algorithm ◽  
Inverse Kinematics ◽  
Fitness Function ◽  
Optimal Solution ◽  
Solution Approach ◽  
The Poor ◽  
Pose Control ◽  
Simulation Results ◽  
Six Degree Of Freedom ◽  
Speed And Accuracy

Solving the inverse kinematics for a manipulator is of great importance to the manipulator's pose control and trajectory planning. Aiming at the poor generality and difficulty of finding an optimal solution from the multiple inverse kinematics solutions, a novel solution approach based on the modified adaptive niche genetic algorithm is proposed in this study. The principle of 'most suppleness' is integrated into the fitness function such that the only optimal solution can be found; The clustering is introduced into the approach for enhancing the generality and the genetic algorithm is improved for increasing the convergence speed and accuracy. Simulation results based on a six degree of freedom manipulator show that the proposed approach is effective and high precision, and can find the optimal solution.

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