A Hybrid Ant Colony Algorithm with a Local Search for the Strongly Correlated Knapsack Problem

2017 ◽  
Author(s):  
Wiem Zouari ◽  
Ines Alaya ◽  
Moncef Tagina
Keyword(s):  
Local Search ◽  
Knapsack Problem ◽  
Ant Colony Algorithm ◽  
Ant Colony ◽  
Strongly Correlated

Deformation Monitoring Model of Jinping-I Hydropower Station Based on NACA-BP Neural Network

2014 ◽  
Vol 704 ◽  
pp. 257-260
Author(s):  
De Wen Cai ◽  
Chen Fei Shao ◽  
Di Kai Wang ◽  
Er Feng Zhao ◽  
Meng Yang
Keyword(s):  
Neural Network ◽  
Local Search ◽  
Prediction Model ◽  
Bp Neural Network ◽  
Ant Colony Algorithm ◽  
Back Propagation ◽  
Deformation Monitoring ◽  
Ant Colony ◽  
Monitoring Data ◽  
Neural Network Modeling

Back Propagation (BP) neural network can learn and store a large number of input-output model nonlinear relationships with simple structure. Niche ant colony algorithm (NACA) combines the ant colony algorithm (ACA) with the niche technology in order to add its local search ability to ACA with preserving the intelligent search ability and robustness of ACA. To optimize predicting model establishment of the dam monitoring data, NACA and BP neural network modeling method are combined to establish a prediction model of horizontal displacement monitoring data. The traditional BP neural network prediction model is established to make a comparison with the NACA. The results show that NACA-BP neural network method can speed up the convergence rate of BP neural network and enhance local search ability and prediction accuracy.

Parallel Ant System Based on OpenMP

2013 ◽  
Vol 765-767 ◽  
pp. 658-661
Author(s):  
Yan Zhang ◽  
Hui Ling Wang ◽  
Xu Li ◽  
Yong Hua Zhang ◽  
Hao Wang
Keyword(s):  
Global Optimization ◽  
Local Search ◽  
Parallel Programming ◽  
Ant Colony Algorithm ◽  
Global Search ◽  
Ant Colony ◽  
Ant System ◽  
Ant Colonies ◽  
Computing Power

To overcome the limitation of precocity and stagnation in classical ant colony algorithm, this article presents a Parallel Ant System Based on OpenMP. The ant colony is divided into three children ant colonies according to the characteristics of natural ant colony multi-group and pheromone updating features of ant colony algorithm. By Open Multi-Processing parallel programming idea, the parallel and cooperating optimization of children ant colonies was obtained. It organically combines local search and global search, makes full use of computing power of multi-core CPU, and improves the efficiency significantly. Contrastive experiments show that the algorithm has a better capability of global optimization than traditional ant colony algorithm.

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.

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.

Sign in / Sign up

Export Citation Format

Share Document