Intelligent Learning Ant Colony Algorithm

2011 ◽  
Vol 48-49 ◽  
pp. 625-631 ◽  
Author(s):  
Jian Hua Ma ◽  
Fa Zhong Tian
Keyword(s):  
Ant Colony Algorithm ◽  
Ant Colony ◽  
Effective Algorithm ◽  
Learning Mechanism ◽  
Numerical Examples ◽  
Improvement Method ◽  
Hard Problems ◽  
Concentration Degree ◽  
The Individual ◽  
Np Hard Problems

Ant colony algorithm is effective algorithm for NP-hard problems, but it also tends to mature early as other evolutionary algorithms. One improvement method of ant colony algorithm is studied in this paper. Intelligent learning ant colony algorithm with the pheromone difference and positive-negative learning mechanism is brought forward to solve TSP. The basic approach of ant colony algorithm is introduced firstly, then we introduced the individual pheromone matrix and positive-negative learning mechanism into ant colony algorithm. Next the steps of intelligent learning ant colony algorithm are given. At last the effectiveness of this algorithm is proved by random numerical examples and typical numerical examples. It is also proved that intelligent ant and learning mechanism will affect concentration degree of pheromone.

scholarly journals Mutation Ant Colony Algorithm of Milk-Run Vehicle Routing Problem with Fastest Completion Time Based on Dynamic Optimization

2013 ◽  
Vol 2013 ◽  
pp. 1-6 ◽  
Author(s):  
Jianhua Ma ◽  
Guohua Sun
Keyword(s):  
Vehicle Routing ◽  
Dynamic Optimization ◽  
Vehicle Routing Problem ◽  
Completion Time ◽  
Ant Colony Algorithm ◽  
Ant Colony ◽  
Routing Problem ◽  
Numerical Examples ◽  
Customer Order ◽  
Milk Run

The objective of vehicle routing problem is usually to minimize the total traveling distance or cost. But in practice, there are a lot of problems needed to minimize the fastest completion time. The milk-run vehicle routing problem (MRVRP) is widely used in milk-run distribution. The mutation ACO is given to solve MRVRP with fastest completion time in this paper. The milk-run VRP with fastest completion time is introduced first, and then the customer division method based on dynamic optimization and split algorithm is given to transform this problem into finding the optimal customer order. At last the mutation ACO is given and the numerical examples verify the effectiveness of the algorithm.

scholarly journals Intelligent Bionic Optimization Algorithm Based on the Growth Characteristics of Tree Branches

2021 ◽  
Vol 15 (2) ◽  
pp. 34-46
Author(s):  
Fei Tang
Keyword(s):  
Genetic Algorithm ◽  
Tree Growth ◽  
Optimization Algorithm ◽  
Ant Colony Algorithm ◽  
Morphological Characteristics ◽  
Optimal Solution ◽  
Shoot Tip ◽  
Experimental Results ◽  
Ant Colony ◽  
The Individual

To improve the performance of bionic algorithms, an intelligent bionic optimization algorithm is proposed based on the morphological characteristics of trees growing toward light. The growth organ of the tree is mapped into the coding of the tree growth algorithm, and the entire tree is formed by selecting the fastest growing individual to form the next level of the tree. When the tree growth reaches a certain level, the individual code of the shoot tip is added to enhance the search ability of the individual shoot tip in the growth space of the entire tree. This method achieves a near-optimal solution. The experimental results were compared with the optimization results of the genetic algorithm and the ant colony algorithm using the classic optimization function. The experimental results show that this algorithm has fewer iterations, a faster convergence speed, higher precision, and a better optimization ability than the genetic algorithm or the ant colony algorithm.

scholarly journals A Intelligent Logistics Inventory Distribution Model Based On Pipeline Network And Ant Colony Algorithm

2018 ◽  
Vol 53 ◽  
pp. 03046
Author(s):  
Jingwen Li ◽  
Yifei Tang ◽  
Jianwu Jiang
Keyword(s):  
Ant Colony Algorithm ◽  
Ant Colony ◽  
Distribution Model ◽  
Efficient Operation ◽  
Logistics Industry ◽  
Pipeline Network ◽  
Distribution Station ◽  
Management Modes ◽  
Intelligent Logistics ◽  
The Individual

With the popularization and application of emerging Internet technologies such as big data and cloud computing, the traditional B2B and B2C warehousing logistics management modes have not achieved synergy between various distribution stations and suppliers, achieving “one-to-one” means a distribution station is supplied by a manufacturer, and a customer is also supplied by a distribution station. The traditional logistics industry model can no longer meet the individual needs of customers. At present, the logistics industry has a series of problems such as slow delivery, slow turnover, high cost and poor service. Based on the theoretical basis of pipeline network and smart logistics, this paper proposes a pipeline network model of intelligent logistics, and improves the ant colony algorithm to improve transportation efficiency, which provides a guarantee for the efficient operation of the intelligent logistics platform.

scholarly journals Finding the shortest path using the ant colony optimization

2017 ◽  
Vol 7 (1.1) ◽  
pp. 392
Author(s):  
K Yella Swamy ◽  
Saranya Gogineni ◽  
Yaswanth Gunturu ◽  
Deepchand Gudapati ◽  
Ramu Tirumalasetti
Keyword(s):  
Ant Colony Optimization ◽  
Optimal Solution ◽  
Ant Colony ◽  
Main Concept ◽  
Pheromone Trails ◽  
Artificial Ants ◽  
Computer Based ◽  
Hard Problems ◽  
Short Time ◽  
Np Hard Problems

An ant colony optimization(ACO) is a techniquewhich is recently introduced ,and it is applied to solve several np-hard problems ,we can get optimal solution in a short time Main concept of the ACO is based on the behavior of ants in their colony for finding a source of food. They will communicate indirectly through pheromone trails. Computer based simulation is can generate good solution by using artificial ants, by using that general behavior we are solving travelling Sale man problem.

Solving NP-Hard Problems with Physarum-Based Ant Colony System

2017 ◽  
Vol 14 (1) ◽  
pp. 108-120 ◽  
Author(s):  
Yuxin Liu ◽  
Chao Gao ◽  
Zili Zhang ◽  
Yuxiao Lu ◽  
Shi Chen ◽  
...  
Keyword(s):  
Ant Colony ◽  
Ant Colony System ◽  
Np Hard ◽  
Hard Problems ◽  
Np Hard Problems

Department of Computer Science and Information Technology, College of Computer Science & Information Technology, Firat University, turkey

2020 ◽  
Vol 1 (1) ◽  
pp. 46-63
Author(s):  
Sam M. Alman
Keyword(s):  
Information Technology ◽  
Computer Science ◽  
Ant Colony Optimization ◽  
Ant Colony Algorithm ◽  
Ant Colony ◽  
Initial State ◽  
Science Field ◽  
Hard Problems ◽  
Technology College ◽  
Science Information

This paper provides uses a new Ant Colony based algorithms called U-Turning Ant colony optimization (U-TACO) for solving one of NP-Hard problems which is widely used in computer science field called Traveling Salesman Problem (TSP). U-Turning Ant colony Optimization based on making partial tour as an initial state for the basic Ant Colony algorithm. This paper provides tables and charts for the results obtained by U-Turning Ant colony Optimization for various TSP problems from the TSPLIB95.

scholarly journals Development of a Convolution-Based Multi-Directional and Parallel Ant Colony Algorithm Considering a Network with Dynamic Topology Changes

2019 ◽  
Vol 9 (18) ◽  
pp. 3646 ◽  
Author(s):  
Eunseo Oh ◽  
Hyunsoo Lee
Keyword(s):  
Complex Network ◽  
Ant Colony Algorithm ◽  
Gaussian Process Regression ◽  
Ant Colony ◽  
Path Generation ◽  
Topology Changes ◽  
Dynamic Topology ◽  
Hard Problems ◽  
Convolution Method ◽  
Topological Changes

While network path generation has been one of the representative Non-deterministic Polynomial-time (NP)-hard problems, changes of network topology invalidate the effectiveness of the existing metaheuristic algorithms. This research proposes a new and efficient path generation framework that considers dynamic topology changes in a complex network. In order to overcome this issue, Multi-directional and Parallel Ant Colony Optimization (MPACO) is proposed. Ant agents are divided into several groups and start at different positions in parallel. Then, Gaussian Process Regression (GPR)-based pheromone update method makes the algorithm more efficient. While the proposed MPACO algorithm is more efficient than the existing ACO algorithm, it is limited in a network with topological changes. In order to overcome the issue, the MPACO algorithm is modified to the Convolution MPACO (CMPACO) algorithm. The proposed algorithm uses the pheromone convolution method using a discrete Gaussian distribution. The proposed pheromone updating method enables the generation of a more efficient network path with comparatively less influence from topological network changes. In order to show the effectiveness of CMPACO, numerical networks considering static and dynamic conditions are tested and compared. The proposed CMPACO algorithm is considered a new and efficient parallel metaheuristic method to consider a complex network with topological changes.

The improved intelligent bionic optimization algorithm based on the growth characteristics of tree branches

2021 ◽  
pp. 1-9
Author(s):  
Fei Tang
Keyword(s):  
Genetic Algorithm ◽  
Tree Growth ◽  
Optimization Algorithm ◽  
Ant Colony Algorithm ◽  
Optimal Solution ◽  
Growth Characteristics ◽  
Ant Colony ◽  
Optimization Function ◽  
Growth Space ◽  
The Individual

To improve the optimization efficiency of the intelligent bionic optimization algorithm, this paper proposes intelligent bionic optimization algorithm based on the growth characteristics of tree branches. Firstly, the growth organ of the tree is mapped into the coding of the tree growth algorithm (intelligent bionic optimization algorithm). Secondly, the entire tree, that is the growing tree, is formed by selecting the individual that grows fast to generate the next level of shoot population. Lastly, if the growing tree reaches a certain level, the individual coding of the shoots is added to enhance the searching ability of the individuals of current generation in the growth tree growth space, so that the algorithm approaches the optimal solution. The experimental results were compared with the optimization results of the genetic algorithm and the ant colony algorithm using the classic optimization function and showed that this algorithm has fewer iterations, a faster convergence speed, higher precision, and a better optimization ability than the genetic algorithm and the ant colony algorithm.

Sign in / Sign up

Export Citation Format

Share Document