Parameters Optimization in SVM Based-On Ant Colony Optimization Algorithm

2010 ◽  
Vol 121-122 ◽  
pp. 470-475 ◽  
Author(s):  
Xiang Ying Liu ◽  
Hui Yan Jiang ◽  
Feng Zhen Tang
Keyword(s):  
Ant Colony Optimization ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Optimization Method ◽  
Parameters Optimization ◽  
Ant Colony ◽  
Trial And Error ◽  
Ant Colony Optimization Algorithm ◽  
Real World Datasets ◽  
Better Than

In this paper ACO (Ant Colony Optimization) algorithm, which is a well-known intelligent optimization method, is applied to selecting parameters for SVM.ACO has the characteristics of positive feedback, parallel mechanism and distributed computation. This paper gives comparison of ACO-SVM, PSO-SVM whose parameters are determined by particle swarm optimization algorithm, and traditional SVM whose parameters are decided through trial and error. The experimental results on real-world datasets show that this proposed method avoids randomness and subjectivity in the traditional SVM. Additionally it is able to gain better parameters which could dedicate to a higher classification accuracy than the PSO-SVM. Results confirm that proposed optimization method is better than the two others.

A New Model of Projection Pursuit Grade Evaluation Model Based on Simulated Annealing Ant Colony Optimization Algorithm

2018 ◽  
Vol 12 (4) ◽  
pp. 69-80
Author(s):  
Gai Zhaomei ◽  
Liu Rentao ◽  
Jiang Qiuxiang
Keyword(s):  
Simulated Annealing ◽  
Ant Colony Optimization ◽  
Optimization Algorithm ◽  
Quality Evaluation ◽  
Evaluation Model ◽  
Projection Pursuit ◽  
Parameters Optimization ◽  
Ant Colony ◽  
Ant Colony Optimization Algorithm ◽  
New Model

Projection pursuit model (PP) is widely used in many fields, especially quality evaluation. One of the biggest shortages of PP was that the projection direction is strongly influenced by relevant parameters. In order to solve this problem, many experts and scholars introduced all kinds of parameters optimization method in PP. Based on the basis of previous studies, the article proposed a new model of projection pursuit grade evaluation model (PPE) integrated with simulated annealing ant colony optimization algorithm (SA-ACO). It provided a new thought and method for quality evaluation research. The case example demonstrated that the accuracy and the effect evaluation of the model was effectively and more objectively and practical in the evaluation of quality.

scholarly journals Optimization of operation sequencing based on GA-Jaya algorithm

2021 ◽  
Vol 2113 (1) ◽  
pp. 012075
Author(s):  
Kai Zheng ◽  
Rui Zhang ◽  
Zhen-Wei Zhu ◽  
Hua-Dong Zhao
Keyword(s):  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Ant Colony Optimization ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Ant Colony ◽  
Jaya Algorithm ◽  
Ant Colony Optimization Algorithm ◽  
Operation Sequencing ◽  
Swarm Optimization

Abstract To solve the operation sequencing problem in CAPP that is a difficult problem, combining the idea of genetic algorithm, an GA-Jaya algorithm is proposed to minimize the total cost. In the GA-Jaya, the population is initialized according to the procedure priority adjacency matrix which makes the population all meet the process priority relationship. Mutation iteration operator and two kinds of crossover iteration operator are designed for process sequence and processing resource evolution. The GA-Jaya algorithm is applied to a typical case, and compared with the existing genetic algorithm, particle swarm optimization algorithm and ant colony optimization algorithm. The results show that the average quality of the solution obtained by the GA-Jaya algorithm is better than the existing genetic algorithm, particle swarm optimization algorithm and ant colony optimization algorithm.

Grid Task Scheduling Strategy Based on Particle Swarm Optimizationand Ant Colony Optimization Algorithm

2010 ◽  
Vol 108-111 ◽  
pp. 392-397
Author(s):  
Peng Cheng Wei ◽  
Xi Shi
Keyword(s):  
Particle Swarm Optimization ◽  
Ant Colony Optimization ◽  
Task Scheduling ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Scheduling Algorithm ◽  
Particle Swarm ◽  
Ant Colony ◽  
Ant Colony Optimization Algorithm ◽  
Swarm Optimization

Based on particle swarm optimization algorithm, this paper presents a grid scheduling optimization algorithm combing the advantages of Ant Colony optimization algorithm. The algorithm processes task scheduling through particle swarm optimization algorithm to get a group of relatively optimal solutions, and then conducts small-area local search with Ant Colony optimization algorithm. Theoretical analysis and results of the simulation experiments show that this scheduling algorithm effectively achieves load balancing of resources with comprehensive advantages in time efficiency and solution accuracy compared to the traditional Ant Colony optimization algorithm and particle swarm optimizationalgorithm, and can be applied to task scheduling in grid computing.

scholarly journals Punching trajectory optimization method for warp-knitted vamp based on improved ant colony optimization algorithm and Radau pseudospectral method

2021 ◽  
Vol 16 ◽  
pp. 155892502110591
Author(s):  
Chi Xinfu ◽  
Li Qiyang ◽  
Zhang Xiaowei ◽  
Sun Yize
Keyword(s):  
Ant Colony Optimization ◽  
Optimization Algorithm ◽  
Trajectory Optimization ◽  
Pseudospectral Method ◽  
Optimal Solution ◽  
Optimization Method ◽  
Feedback Mechanism ◽  
Ant Colony ◽  
Ant Colony Optimization Algorithm ◽  
Low Efficiency

Aiming at the problems of complex trajectory, low efficiency and high operational difficulty of the robot in multi-point punching of warp-knitted vamp, a method of optimizing punching trajectory based on improved ant colony optimization algorithm and Radau pseudospectral method is proposed. After obtaining the position coordinates of punching points, an improved ant colony optimization algorithm is used to calculate the punching sequence of the shortest path through all punching points, and then Radau pseudospectral method is used to solve the optimal trajectory of the laser punching robot. Improved ant colony optimization algorithm combines a distributed calculation method and the positive feedback mechanism. Radau pseudospectral method can transform the optimal control problems into nonlinear programming problems, and the combination of the two can quickly and reliably obtain the optimal solution. To verify the method, under the condition of selecting the same number and location of punching points, the experiments of Radau pseudospectral method to solve the trajectory planning of laser punching robot is carried out. The experimental results show that improved ant colony optimization algorithm can calculate the path of the vamp punching point in a shorter time and with high accuracy. Radau pseudospectral method can obtain smooth trajectories satisfying various constraints, which can meet the requirements of accuracy and efficiency in practical production.

Modeling and optimizing logistics, information, economic and financial intercluster cooperation using the ant colony optimization algorithm

2020 ◽  
Vol 26 (11) ◽  
pp. 2427-2447
Author(s):  
S.N. Yashin ◽  
E.V. Koshelev ◽  
S.A. Borisov
Keyword(s):  
Ant Colony Optimization ◽  
Optimization Algorithm ◽  
Population Decline ◽  
Innovation System ◽  
Federal District ◽  
Original Model ◽  
Ant Colony ◽  
Industrial Agglomeration ◽  
Ant Colony Optimization Algorithm

Subject. This article discusses the issues related to the creation of a technology of modeling and optimization of economic, financial, information, and logistics cluster-cluster cooperation within a federal district. Objectives. The article aims to propose a model for determining the optimal center of industrial agglomeration for innovation and industry clusters located in a federal district. Methods. For the study, we used the ant colony optimization algorithm. Results. The article proposes an original model of cluster-cluster cooperation, showing the best version of industrial agglomeration, the cities of Samara, Ulyanovsk, and Dimitrovgrad, for the Volga Federal District as a case study. Conclusions. If the industrial agglomeration center is located in these three cities, the cutting of the overall transportation costs and natural population decline in the Volga Federal District will make it possible to qualitatively improve the foresight of evolution of the large innovation system of the district under study.

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