Study of Sintering Blending Based on Swarm Intelligence Optimization Algorithm

2012 ◽  
Vol 198-199 ◽  
pp. 1550-1553 ◽  
Author(s):  
Hui Zhao ◽  
Ming Wang ◽  
Hong Jun Wang ◽  
You Jun Yue
Keyword(s):  
Exact Solution ◽  
Optimization Algorithm ◽  
Ant Colony Algorithm ◽  
Particle Swarm ◽  
Optimization Method ◽  
Feedback Mechanism ◽  
Ant Colony ◽  
Positive Feedback Mechanism ◽  
Swarm Intelligence Optimization

The sintering blending is a complex nonlinear optimization problem. The traditional single algorithm can not meet the requirement of good quality of sinter and lowest costs well. So, a hybrid optimization method of particle swarm and ant colony algorithm was proposed. The method gives full play to the global convergence of particle swarm optimization algorithm, takes it as a preliminary search, then use the positive feedback mechanism of ant colony algorithm for the exact solution, to make these two algorithms to reach a complementary, in order to get a rapid exact solution. The simulation results show that the proposed hybrid algorithm has fast convergence and high accuracy, which can effectively reduce the sintering cost.

Parallel Ant Colony Optimization Algorithm Based on Multiplicate Pheromon Declining

2012 ◽  
Vol 433-440 ◽  
pp. 3577-3583
Author(s):  
Yan Zhang ◽  
Hao Wang ◽  
Yong Hua Zhang ◽  
Yun Chen ◽  
Xu Li
Keyword(s):  
Ant Colony Optimization ◽  
Optimization Algorithm ◽  
Ant Colony Algorithm ◽  
Optimal Path ◽  
Ant Colony ◽  
Ant Colony Optimization Algorithm ◽  
Computing Power ◽  
Slow Convergence ◽  
The Impact

To overcome the defect of the classical ant colony algorithm’s slow convergence speed, and its vulnerability to local optimization, the authors propose Parallel Ant Colony Optimization Algorithm Based on Multiplicate Pheromon Declining to solve Traveling Salesman Problem according to the characteristics of natural ant colony multi-group and pheromone updating features of ant colony algorithm, combined with OpenMP parallel programming idea. The new algorithm combines three different pheromone updating methods to make a new declining pheromone updating method. It effectively reduces the impact of pheromone on the non-optimal path in the ants parade loop to subsequent ants and improves the parade quality of subsequent ants. It makes full use of multi-core CPU's computing power and improves the efficiency significantly. The new algorithm is compared with ACO through experiments. The results show that the new algorithm has faster convergence rate and better ability of global optimization than ACO.

Research on Path Optimization of Automated Warehouse Based on Ant Colony Algorithm

2011 ◽  
Vol 201-203 ◽  
pp. 1112-1115
Author(s):  
Hao Ping Li ◽  
Zi Fan Fang ◽  
Ying Wang
Keyword(s):  
Mathematical Model ◽  
Energy Consumption ◽  
Optimization Algorithm ◽  
Ant Colony Algorithm ◽  
Operation Time ◽  
Optimization Method ◽  
Ant Colony ◽  
Path Optimization ◽  
Operating Costs ◽  
Reduce Energy Consumption

Based on analysis of the cargo selecting strategy of fixed shelf automated warehouse, the idea of path optimization is put forward and the stacker path optimization method is studied. A mathematical model of stacker operation path optimization is built to minimize the length of operation path and the operation time. The model is solved by using the ant colony optimization method. Simulation shows that chosen stacker operation path by using the optimization model and optimization algorithm, can not only reduce energy consumption and warehouse operating costs, but also improve the efficiency of goods storage.

scholarly journals Decision of Multimodal Transportation Scheme Based on Swarm Intelligence

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Kai Lei ◽  
Xiaoning Zhu ◽  
Jianfei Hou ◽  
Wencheng Huang
Keyword(s):  
Particle Swarm Optimization ◽  
Swarm Intelligence ◽  
Optimization Algorithm ◽  
Ant Colony Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Ant Colony ◽  
Swarm Optimization ◽  
Multimodal Transportation ◽  
Better Than

In this paper, some basic concepts of multimodal transportation and swarm intelligence were described and reviewed and analyzed related literatures of multimodal transportation scheme decision and swarm intelligence methods application areas. Then, this paper established a multimodal transportation scheme decision optimization mathematical model based on transportation costs, transportation time, and transportation risks, explained relevant parameters and the constraints of the model in detail, and used the weight coefficient to transform the multiobjective optimization problems into a single objective optimization transportation scheme decision problem. Then, this paper is proposed by combining particle swarm optimization algorithm and ant colony algorithm (PSACO) to solve the combinatorial optimization problem of multimodal transportation scheme decision for the first time; this algorithm effectively combines the advantages of particle swarm optimization algorithm and ant colony algorithm. The solution shows that the PSACO algorithm has two algorithms’ advantages and makes up their own problems; PSACO algorithm is better than ant colony algorithm in time efficiency and its accuracy is better than that of the particle swarm optimization algorithm, which is proved to be an effective heuristic algorithm to solve the problem about multimodal transportation scheme decision, and it can provide economical, reasonable, and safe transportation plan reference for the transportation decision makers.

scholarly journals Ant Colony Optimization Algorithm Model Based on the Continuous Space

2016 ◽  
Vol 12 (12) ◽  
pp. 27 ◽  
Author(s):  
Xuepeng Huang
Keyword(s):  
Ant Colony Algorithm ◽  
Feedback Mechanism ◽  
Ant Colony ◽  
Optimization Strategy ◽  
Ant Colony Optimization Algorithm ◽  
Continuous Space ◽  
Distributed Parallel Computing ◽  
Branching Factor ◽  
Positive Feedback Mechanism ◽  
Distribution Algorithms

Ant colony algorithm is a heuristic algorithm which is　fit for solving complicated combination optimization.It showed great advantage on solving combinatorial optimization problem since it was proposed. The algorithm uses distributed parallel computing and positive feedback mechanism, and is easy to combine with other algorithms.This ant colony algorithm has already been widespread used in the field of discrete space optimization, however, is has been rarely used for continuous space optimization question.On the basis of basic ant colony algorithm principles and mathematical model, this paper proposes an ant colony algorithm for solving continuous space optimization question.Comparing with the ant colony algorithm, the new algorithm improves the algorithm in aspects of ant colony initialization, information density function, distribution algorithms, direction of ant colonymotion, and so on. The new algorithm uses multiple optimization strategy, such as polynomial time reduction and branching factor, and improves the ant colony algorithm effectively.

An Improved Ant Colony Algorithm Applied in the TSP Problem

2013 ◽  
Vol 380-384 ◽  
pp. 1738-1741
Author(s):  
Meng Lan Wang
Keyword(s):  
Ant Colony Algorithm ◽  
Group Behavior ◽  
Feedback Mechanism ◽  
Ant Colony ◽  
Optimal Solutions ◽  
Heuristic Information ◽  
Random Probability ◽  
Positive Feedback Mechanism ◽  
Maximum Minimum ◽  
Improved Ant Colony Algorithm

Ant colony algorithm is a kind of intelligent algorithm imitating the group behavior of ants. The positive feedback mechanism is not only its advantage which makes the ant colony algorithm quickly converge to optimal solutions of a problem, but also its defect which makes it easy to fall into the local optimal solutions. ACS and MMAS are the two typically improved ant algorithms by introducing the pseudo random probability selection rule and maximum-minimum pheromone restriction rule to accelerate the converging speed of this algorithm and avoid falling into local optimal solutions. At present, there is no algorithm put forward to improve the algorithm using the effect of the heuristic information. This paper presents an improved ant colony algorithm based on the heuristic information of direction, and provides a new idea for the study on the improved ant colony algorithm.

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.

scholarly journals HYBRIDIZATION OF ANT COLONY ALGORITHM AND PARTICLE SWARM OPTIMIZATION ALGORITHM FOR REDUCTION OF REAL POWER LOSS

2018 ◽  
Vol 6 (12) ◽  
pp. 121-127
Author(s):  
K. Lenin
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Power Loss ◽  
Ant Colony Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Reactive Power ◽  
Particle Swarm ◽  
Ant Colony ◽  
Swarm Optimization ◽  
Real Power

In this work Ant colony optimization algorithm (ACO) & particle swarm optimization (PSO) algorithm has been hybridized (called as APA) to solve the optimal reactive power problem. In this algorithm, initial optimization is achieved by particle swarm optimization algorithm and then the optimization process is carry out by ACO around the best solution found by PSO to finely explore the design space. In order to evaluate the proposed APA, it has been tested on IEEE 300 bus system and compared to other standard algorithms. Simulations results show that proposed APA algorithm performs well in reducing the real power loss.

scholarly journals TSP Solving Utilizing Improved Ant Colony Algorithm

2021 ◽  
Vol 2129 (1) ◽  
pp. 012026
Author(s):  
Xuan-Shi Yao ◽  
Yun Ou ◽  
Kai-Qing Zhou
Keyword(s):  
Mathematical Model ◽  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Ant Colony Algorithm ◽  
Optimal Path ◽  
Particle Swarm ◽  
Ant Colony ◽  
Ant Colony Optimization Algorithm ◽  
Swarm Optimization ◽  
Pheromone Concentration

Abstract To solve the premature issue of TSP solving using the ant colony optimization algorithm (ACO), this paper proposes an improved ACO using particle swarm optimization (PSO) to solve the classic traveling salesman problem (TSP). The algorithm’s strategy includes three stages: firstly, establishing a mathematical model according to the optimization objective, and then solving the optimal path obtained by the particle swarm optimization algorithm. Finally, the pheromone concentration of this path in the ant colony mathematical model is enhanced according to the particle swarm optimization algorithm’s optimal path. A classic TSP case is used to compare the PSO and ACO. The results show that the proposed improved algorithm has a faster convergence speed and can converge to the optimal global solution, and its performance is better than that of ACO and PSO.

Continuous Ant Colony Algorithm

2011 ◽  
Vol 308-310 ◽  
pp. 1008-1011 ◽  
Author(s):  
Wei Gao
Keyword(s):  
Swarm Intelligence ◽  
Optimization Algorithm ◽  
Ant Colony Algorithm ◽  
Continuous Optimization ◽  
Evolutionary Programming ◽  
Optimization Method ◽  
Good Combination ◽  
Ant Colony ◽  
Combination Optimization ◽  
Searching Performance

Ant Colony Algorithm is a new bionics optimization algorithm from mimic the swarm intelligence of ant colony behavior. And it is a very good combination optimization method. To extend the ant colony algorithm, and to improve the searching performance, from the connections of continuous optimization and searching process of ant colony algorithm, one new Continuous Ant Colony Algorithm is proposed. To verify the new algorithm, the typical functions, such as Schaffer function and “Needle-in-a-haystack” function, are all used. And then, the results of new algorithm are compared with that of immunized evolutionary programming proposed by author.

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