The Combination between the Individual Factors and the Collective Experience for Ultimate Optimization Learning Path using Ant Colony Algorithm

2018 ◽  
Vol 8 (4) ◽  
pp. 1198 ◽  
Author(s):  
Imane Kamsa ◽  
Rachid Elouahbi ◽  
Fatima El khoukhi
Keyword(s):  
Ant Colony Algorithm ◽  
Ant Colony ◽  
Individual Factors ◽  
Learning Path ◽  
Collective Experience ◽  
The Individual

scholarly journals Adaptive Learning Model Based on Ant Colony Algorithm

2019 ◽  
Vol 14 (01) ◽  
pp. 49 ◽  
Author(s):  
Rongxia Li
Keyword(s):  
Adaptive Learning ◽  
Ant Colony Algorithm ◽  
Learning Model ◽  
Learning Objects ◽  
Ant Colony ◽  
Learning Path ◽  
Knowledge Levels ◽  
Actual Learning ◽  
Learning Frameworks ◽  
Adaptive Online Learning

To better respond to people’s demands for multimedia learning, appropriate learn-ing paths should be offered based on their actual learning demands and different knowledge levels. Adaptive online learning model integrates and improves exist-ing learning frameworks to offer a set of knowledge paths that can cater to dif-fer7ent preferences, tastes, and knowledge levels of learners, no need for them to be aware of this. Based on the improved ant colony algorithm, an adaptive learn-ing system model that can satisfy learners’ demands is built herein with reference to the foraging approach of ants to traverse the paths, thereby to find the best learning path, while the classification method for some learning objects can de-termine the search parameters. This innovative approach proposed hereof can help improve learners' academic performance and learning efficiency.

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.

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.

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.

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