Ant Colony Optimization Algorithm for the 0-1 Knapsack Problem Based on Genetic Operators

2011 ◽  
Vol 230-232 ◽  
pp. 973-977 ◽  
Author(s):  
Zhi Jun Hu ◽  
Rong Li
Keyword(s):  
Knapsack Problem ◽  
Ant Colony Algorithm ◽  
Analysis Of Algorithms ◽  
Dynamic Programming Algorithm ◽  
Ant Colony ◽  
Programming Algorithm ◽  
Ant Colony Optimization Algorithm ◽  
Genetic Operators ◽  
Optimistic Algorithm ◽  
Improved Ant Colony Algorithm

0-1 knapsack problem is a typical combinatorial optimization question in the design and analysis of algorithms. The mathematical description of the knapsack problem is given in theory. The 0-1 knapsack problem is solved by ant colony optimistic algorithm that is improved by introducing genetic operators. To solve the 0-1 knapsack problem with the improved ant colony algorithm, experimental results of numerical simulations, compared with greedy algorithm and dynamic programming algorithm, have shown obvious advantages in efficiency and accuracy on the knapsack problem.

Enhancing Collaborative Filtering Recommendation by Utilizing Improved Ant Colony Optimization Algorithm

2014 ◽  
Vol 556-562 ◽  
pp. 3793-3799
Author(s):  
Zhong Liang Li ◽  
Chen Xiao Hu ◽  
Xu Yang Wei ◽  
Teng Fei Zou ◽  
Hao Ran Zhang ◽  
...  
Keyword(s):  
Collaborative Filtering ◽  
Ant Colony Algorithm ◽  
Recommendation System ◽  
Ant Colony ◽  
Personalized Recommendation ◽  
Access Time ◽  
Ant Colony Optimization Algorithm ◽  
Access Frequency ◽  
Collaborative Filtering Algorithm ◽  
Improved Ant Colony Algorithm

Collaborative filtering (CF) is the most widely used and successful personalized recommendation technology in web usage mining. The traditional collaborative filtering algorithm based on user static evaluation of the item's neighbour to predict changes of the users’ interests, however, the user’s interest will make a difference over time. Taking the dynamic changes the user’s interest into account in the process, this paper presents a dynamic collaborative filtering recommendation method based on improved ant colony algorithm (EACF). Improved ant colony algorithm takes into account the user access time and access frequency, which can be more representative of the true interests of users. When generating the recommendation, this method not only takes into account the item’s score, but also will take into account intensity of “interest pheromone” on each item. Experimental results show that the EACF can significantly improve the prediction accuracy of the recommendation system compared with traditional CF.

An Optimized Algorithm of Numerical Cutting-Path Control in Garment Manufacturing

2013 ◽  
Vol 796 ◽  
pp. 454-457 ◽  
Author(s):  
Jing Ye ◽  
Zhi Ge Chen
Keyword(s):  
Dynamic Programming ◽  
Ant Colony Algorithm ◽  
Travelling Salesman Problem ◽  
Dynamic Programming Algorithm ◽  
Ant Colony ◽  
Numerical Control ◽  
Programming Algorithm ◽  
Intelligence Algorithm ◽  
Hybrid Intelligence ◽  
Cutting Path

The garment cutting is a key process during the garment production. Most companies apply the manual labor or simple mechanical aids to achieve the goals. While these methods cost much time and labor. More and more automatic cutting equipment is applied to the garment cutting so as to save time, labor and materials. During the process of cutting, some problems are coming up, especially the cutting path. The cutting path of the garment numerical control cutter is regarded as generalized travelling salesman problem (GTSP). The garment contours can be regarded as the set of cities, and the nodes of a single contour can be regarded as cities. The cutter visits every contour exactly once. A hybrid intelligence algorithm was proposed to solve the problem. The ant colony algorithm was applied to a selected cutting path arbitrarily, an optimal contour sequence was found. Then the garment contour sequences shortest path was transformed into multi-segment graph shortest problem which is solved with the dynamic programming algorithm in order to optimize the knifes in-out point. The final optimal cutting path was constructed with ant colony optimization algorithm and dynamic programming algorithm. The practical application shows that the hybrid intelligence algorithm has satisfactory solution quality.

Ant Colony Algorithm Based on Semantic Relations and its Applications

2014 ◽  
Vol 644-650 ◽  
pp. 2076-2080
Author(s):  
Yong Jian Yang ◽  
Jiu Xuan An ◽  
Hong Ying Han
Keyword(s):  
Ant Colony Optimization ◽  
Ant Colony Algorithm ◽  
Good Method ◽  
Ant Colony ◽  
Semantic Relations ◽  
Ant Colony Optimization Algorithm ◽  
Multi Stage ◽  
The Traveling Salesman Problem ◽  
Improved Ant Colony Algorithm ◽  
Improved Algorithm

Ant colony optimization algorithm (ACO) is a good method to solve complex multi-stage decision problems. But this algorithm is easy to fall into the local minimum points and has slowly convergence speed, According to the semantic relations, an improved ant colony algorithm has been proposed in this paper. In contrast with the tradition algorithm, the improved algorithm is added with a new operator to update crucial parameters. The new operator is to find out the potential semantic relations behind the history information based on ontology technology. Ant colony optimization can be applied to many engineering fields,taking the Traveling Salesman Problem (TSP) as example, Our experiments show accuracy of improved ant colony algorithm that is superior to that obtained by the other classical versions, and competitive or better than the results achieved by the compared algorithm, this improved algorithm also can improve the searching efficiency.

MapReduce-Based Ant Colony Optimization Algorithm for Multi-Dimensional Knapsack Problem

2013 ◽  
Vol 380-384 ◽  
pp. 1877-1880 ◽  
Author(s):  
Rui Tao Liu ◽  
Xiu Jian Lv
Keyword(s):  
Ant Colony Optimization ◽  
Knapsack Problem ◽  
Large Scale ◽  
Ant Colony Algorithm ◽  
Search Time ◽  
Ant Colony ◽  
Ant Colony Optimization Algorithm ◽  
Processing Power ◽  
Crossover And Mutation ◽  
Computing Simulation

This paper uses MapReduce parallel programming mode to make the Ant Colony Optimization (ACO) algorithm parallel and bring forward the MapReduce-based improved ACO for Multi-dimensional Knapsack Problem (MKP). A variety of techniques, such as change the probability calculation of the timing, roulette, crossover and mutation, are applied for improving the drawback of the ACO and complexity of the algorithm is greatly reduced. It is applied to distributed parallel as to solve the large-scale MKP in cloud computing. Simulation experimental results show that the algorithm can improve the defects of long search time for ant colony algorithm and the processing power for large-scale problems.

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