scholarly journals Ant-Set: A Subset-Oriented Ant Colony Optimization Algorithm for the Set Covering Problem

2020 ◽  
Vol 26 (2) ◽  
pp. 293-316
Author(s):  
Murilo Falleiros Lemos Schmitt ◽  
Mauro Mulati ◽  
Ademir Constantino ◽  
Fábio Hernandes ◽  
Tony Hild
Keyword(s):  
Local Search ◽  
Ant Colony Optimization ◽  
Optimization Algorithm ◽  
Ant Colony ◽  
Set Covering ◽  
Covering Problem ◽  
Set Covering Problem ◽  
Ant Colony Optimization Algorithm ◽  
The Impact

This paper proposes an algorithm for the set covering problem based on the metaheuristic Ant Colony Optimization (ACO) called Ant-Set, which uses a lineoriented approach and a novelty pheromone manipulation based on the connections between components of the construction graph, while also applying a local search. The algorithm is compared with other ACO-based approaches. The results obtained show the effectiveness of the algorithm and the impact of the pheromone manipulation.

An Improved and Realized Volatility Strategy of the Ant Colony Optimization Algorithm

2013 ◽  
Vol 389 ◽  
pp. 849-853
Author(s):  
Fang Song Cui ◽  
Wei Feng ◽  
Da Zhi Pan ◽  
Guo Zhong Cheng ◽  
Shuang Yang
Keyword(s):  
Ant Colony Optimization ◽  
Optimization Algorithm ◽  
Optimal Solution ◽  
Ant Colony ◽  
Ant System ◽  
Ant Colony Optimization Algorithm ◽  
Volatility Model ◽  
Local Optimal Solution ◽  
The Impact ◽  
Improved Algorithm

In order to overcome the shortcomings of precocity and stagnation in ant colony optimization algorithm, an improved algorithm is presented. Considering the impact that the distance between cities on volatility coefficient, this study presents an model of adjusting volatility coefficient called Volatility Model based on ant colony optimization (ACO) and Max-Min ant system. There are simulation experiments about TSP cases in TSPLIB, the results show that the improved algorithm effectively overcomes the shortcoming of easily getting an local optimal solution, and the average solutions are superior to ACO and Max-Min ant system.

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.

New ideas for applying ant colony optimization to the set covering problem

2010 ◽  
Vol 58 (4) ◽  
pp. 774-784 ◽  
Author(s):  
Zhi-Gang Ren ◽  
Zu-Ren Feng ◽  
Liang-Jun Ke ◽  
Zhao-Jun Zhang
Keyword(s):  
Ant Colony Optimization ◽  
Ant Colony ◽  
Set Covering ◽  
Covering Problem ◽  
Set Covering Problem ◽  
New Ideas

Modeling and optimizing logistics, information, economic and financial intercluster cooperation using the ant colony optimization algorithm

2020 ◽  
Vol 26 (11) ◽  
pp. 2427-2447
Author(s):  
S.N. Yashin ◽  
E.V. Koshelev ◽  
S.A. Borisov
Keyword(s):  
Ant Colony Optimization ◽  
Optimization Algorithm ◽  
Population Decline ◽  
Innovation System ◽  
Federal District ◽  
Original Model ◽  
Ant Colony ◽  
Industrial Agglomeration ◽  
Ant Colony Optimization Algorithm

Subject. This article discusses the issues related to the creation of a technology of modeling and optimization of economic, financial, information, and logistics cluster-cluster cooperation within a federal district. Objectives. The article aims to propose a model for determining the optimal center of industrial agglomeration for innovation and industry clusters located in a federal district. Methods. For the study, we used the ant colony optimization algorithm. Results. The article proposes an original model of cluster-cluster cooperation, showing the best version of industrial agglomeration, the cities of Samara, Ulyanovsk, and Dimitrovgrad, for the Volga Federal District as a case study. Conclusions. If the industrial agglomeration center is located in these three cities, the cutting of the overall transportation costs and natural population decline in the Volga Federal District will make it possible to qualitatively improve the foresight of evolution of the large innovation system of the district under study.

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