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.

An Improved and Realized Volatility Strategy of the Ant Colony Optimization Algorithm

2013 ◽  
Vol 389 ◽  
pp. 849-853
Author(s):  
Fang Song Cui ◽  
Wei Feng ◽  
Da Zhi Pan ◽  
Guo Zhong Cheng ◽  
Shuang Yang
Keyword(s):  
Ant Colony Optimization ◽  
Optimization Algorithm ◽  
Optimal Solution ◽  
Ant Colony ◽  
Ant System ◽  
Ant Colony Optimization Algorithm ◽  
Volatility Model ◽  
Local Optimal Solution ◽  
The Impact ◽  
Improved Algorithm

In order to overcome the shortcomings of precocity and stagnation in ant colony optimization algorithm, an improved algorithm is presented. Considering the impact that the distance between cities on volatility coefficient, this study presents an model of adjusting volatility coefficient called Volatility Model based on ant colony optimization (ACO) and Max-Min ant system. There are simulation experiments about TSP cases in TSPLIB, the results show that the improved algorithm effectively overcomes the shortcoming of easily getting an local optimal solution, and the average solutions are superior to ACO and Max-Min ant system.

scholarly journals A Modified Ant Colony Optimization Algorithm for Solving a Transportation Problem

2020 ◽  
pp. 83-101
Author(s):  
E. M. U. S. B. Ekanayake ◽  
S. P. C. Perera ◽  
W. B. Daundasekara ◽  
Z. A. M. S. Juman
Keyword(s):  
Ant Colony Optimization ◽  
Optimization Algorithm ◽  
Transportation Problem ◽  
Numerical Study ◽  
Optimal Solution ◽  
Ant Colony ◽  
Solution Technique ◽  
Ant Colony Optimization Algorithm ◽  
Total Cost ◽  
Number Of Iterations

Transportation of products from sources to destinations with minimal total cost plays a key role in logistics and supply chain management. The transportation problem (TP) is an extraordinary sort of Linear Programming problem where the objective is to minimize the total cost of disseminating resources from several various sources to several destinations. Initial feasible solution (IFS) acts as a foundation of an optimal cost solution technique to any TP. Better is the IFS lesser is the number of iterations to reach the final optimal solution. This paper presents a meta-heuristic algorithm, modified ant colony optimization algorithm (MACOA) to attain an IFS to a Transportation Problem. The proposed algorithm is straightforward, simple to execute, and gives us closeness optimal solutions in a finite number of iterations. The efficiency of this algorithm is likewise been advocated by solving validity and applicability examples An extensive numerical study is carried out to see the potential significance of our modified ant colony optimization algorithm (MACOA). The comparative assessment shows that both the MACOA and the existing JHM are efficient as compared to the studied approaches of this paper in terms of the quality of the solution. However, in practice, when researchers and practitioners deal with large-sized transportation problems, we urge them to use our proposed MACOA due to the time-consuming computation of JHM. Therefore this finding is important in saving time and resources for minimization of transportation costs and optimizing transportation processes which could help significantly to improve the organization’s position in the market.

scholarly journals Comparison Performance of Genetic Algorithm and Ant Colony Optimization in Course Scheduling Optimizing

2016 ◽  
Vol 3 (2) ◽  
pp. 149-158 ◽  
Author(s):  
Imam Ahmad Ashari ◽  
Much Aziz Muslim ◽  
Alamsyah Alamsyah
Keyword(s):  
Genetic Algorithm ◽  
Ant Colony Optimization ◽  
Optimization Algorithm ◽  
Optimal Solution ◽  
Optimization Techniques ◽  
Ant Colony ◽  
Scheduling Problems ◽  
Ant Colony Optimization Algorithm ◽  
Course Scheduling ◽  
The University

Scheduling problems at the university is a complex type of scheduling problems. The scheduling process should be carried out at every turn of the semester's. The core of the problem of scheduling courses at the university is that the number of components that need to be considered in making the schedule, some of the components was made up of students, lecturers, time and a room with due regard to the limits and certain conditions so that no collision in the schedule such as mashed room, mashed lecturer and others. To resolve a scheduling problem most appropriate technique used is the technique of optimization. Optimization techniques can give the best results desired. Metaheuristic algorithm is an algorithm that has a lot of ways to solve the problems to the very limit the optimal solution. In this paper, we use a genetic algorithm and ant colony optimization algorithm is an algorithm metaheuristic to solve the problem of course scheduling. The two algorithm will be tested and compared to get performance is the best. The algorithm was tested using data schedule courses of the university in Semarang. From the experimental results we conclude that the genetic algorithm has better performance than the ant colony optimization algorithm in solving the case of course scheduling.

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 Hierarchical Approach to the Capacitated Single Allocation p-Hub Median Problem with Direct Shipment

2012 ◽  
Vol 209-211 ◽  
pp. 807-813
Author(s):  
Ji Ung Sun ◽  
Don Ki Baek
Keyword(s):  
Ant Colony Optimization ◽  
Optimization Algorithm ◽  
Optimal Solution ◽  
Ant Colony ◽  
Network Design Problem ◽  
Computational Time ◽  
Fixed Cost ◽  
Ant Colony Optimization Algorithm ◽  
Median Problem ◽  
Single Allocation

In this paper we consider a capacitated single allocation p-hub median problem with direct shipment (CSApHMPwD). We determine the location of p hubs, the allocation of non-hub nodes to hubs, and direct shipment paths in the network. This problem is formulated as 0-1 integer programming model with the objective of the minimum total transportation cost and the fixed cost associated with the establishment of hubs. An optimal solution is found using CPLEX for the small sized problems. Since the CSApHMPwD is NP-hard, it is difficult to obtain optimal solution within a reasonable computational time. Therefore, an ant colony optimization algorithm is developed which solves hub selection and node allocation problem hierarchically. Its performance is examined through a comparative study. The experimental results show that the proposed ant colony optimization algorithm can be a viable solution method for the capacitated hub and spoke network design problem.

scholarly journals A Modified Ant Colony Optimization Algorithm for Power Scheduling

2019 ◽  
Vol 8 (11) ◽  
pp. 1083-1088
Keyword(s):  
Power System ◽  
Ant Colony Optimization ◽  
Optimization Algorithm ◽  
Peak Load ◽  
Optimal Solution ◽  
Ant Colony ◽  
Present System ◽  
Ant Colony Optimization Algorithm ◽  
Efficient Manner ◽  
Optimal Output

The purpose of this work is to solve the problems related to the power system in an efficient manner by assigning the optimal values. To meet the demand loads with good quality and quantity is a challenging problem in the field of the power system. Artificial Neural Network is realized in the field of energy management and load scheduling. The Backpropagation algorithm is used for the training purpose. It has the aspects of the quick meeting on the local bests but it gets stuck in local minima. To overcome this drawback an Ant Colony Optimization algorithm is presented to allocate optimal output values for the power system. This has the capacity for searching the global optimal solution. Present work modifies the ant colony optimization algorithm with backpropagation. This hybrid algorithm accelerates the network and improves its accuracy. The ant colony optimization algorithms provide an accurate optimal combination of weights and then use backpropagation technique to obtain the accurate optimal solution rapidly. The result shows that the present system is more efficient and effective. These algorithms significantly reduce the peak load and minimize the energy consumption cost

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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