An Ant Colony System Metaheuristic Applied to a Cooperative of Recyclable Materials of Sorocaba: A Case Study

2019 ◽  
pp. 79-97
Author(s):  
Gregory Tonin Santos ◽  
Luiza Amalia Pinto Cantão ◽  
Renato Fernandes Cantão
Keyword(s):  
Ant Colony ◽  
Ant Colony System

scholarly journals Determination of the Shortest Route on the Distribution System using Ant Colony Optimization (ACO) Algorithm (Case Study: Alfamidi Palu Branch – PT. Midi Utama Indonesia)

2021 ◽  
Vol 1 (2) ◽  
pp. 84-93
Author(s):  
Nabila Dwi Indria ◽  
Junaidi Junaidi ◽  
Iut Tri Utami
Keyword(s):  
Ant Colony Optimization ◽  
Distribution System ◽  
Ant Colony ◽  
Ant Colony System ◽  
Ant Colonies ◽  
Status Transition ◽  
Shortest Route ◽  
Transition Rules

The distribution system of goods is one of the most important parts for every company. The company certainly has many route options to visit, and this is expected to be conducted efficiently in terms of time. In the distribution of goods by Alfamidi company in Palu City which has 51 outlets include into the category of Traveling Salesman Problem (TSP) because of many route options that can be visited. The problem can be solved by employing the Ant Colony Optimization (ACO) method which is one of the algorithms Ant Colony System (ACS). The ACS acquires principles based on the behavior of ant colonies and applies three characteristics to determine the shortest route namely status transition rules, local pheromone renewal and global pheromones. The result showed that the shortest route of the distribution of goods based on the calculation of selected iterations was ant 1 with the shortest total distance obtained 86.98 km.

scholarly journals ACS-TS: train scheduling using ant colony system

2006 ◽  
Vol 2006 ◽  
pp. 1-28 ◽  
Author(s):  
Keivan Ghoseiri ◽  
Fahimeh Morshedsolouk
Keyword(s):  
Mathematical Model ◽  
Traveling Salesman ◽  
Ant Colony ◽  
Ant Colony System ◽  
Scheduling Problem ◽  
Train Scheduling ◽  
Numerical Examples ◽  
Accuracy Comparison ◽  
Time Savings

This paper develops an algorithm for the train scheduling problem using the ant colony system metaheuristic called ACS-TS. At first, a mathematical model for a kind of train scheduling problem is developed and then the algorithm based on ACS is presented to solve the problem. The problem is considered as a traveling salesman problem (TSP) wherein cities represent the trains. ACS determines the sequence of trains dispatched on the graph of the TSP. Using the sequences obtained and removing the collisions incurred, train scheduling is determined. Numerical examples in small and medium sizes are solved using ACS-TS and compared to exact optimum solutions to check for quality and accuracy. Comparison of the solutions shows that ACS-TS results in good quality and time savings. A case study is presented to illustrate the solution.

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.

scholarly journals Path planning for mobile robot using an enhanced ant colony optimization and path geometric optimization

2021 ◽  
Vol 18 (3) ◽  
pp. 172988142110192
Author(s):  
Songcan Zhang ◽  
Jiexin Pu ◽  
Yanna Si ◽  
Lifan Sun
Keyword(s):  
Path Planning ◽  
Convergence Rate ◽  
Mobile Robot ◽  
Local Optimization ◽  
Ant Colony ◽  
Ant Colony System ◽  
Geometric Features ◽  
Robot Path Planning ◽  
Computation Efficiency ◽  
Robot Path

Path planning of mobile robots in complex environments is the most challenging research. A hybrid approach combining the enhanced ant colony system with the local optimization algorithm based on path geometric features, called EACSPGO, has been presented in this study for mobile robot path planning. Firstly, the simplified model of pheromone diffusion, the pheromone initialization strategy of unequal allocation, and the adaptive pheromone update mechanism have been simultaneously introduced to enhance the classical ant colony algorithm, thus providing a significant improvement in the computation efficiency and the quality of the solutions. A local optimization method based on path geometric features has been designed to further optimize the initial path and achieve a good convergence rate. Finally, the performance and advantages of the proposed approach have been verified by a series of tests in the mobile robot path planning. The simulation results demonstrate that the presented EACSPGO approach provides better solutions, adaptability, stability, and faster convergence rate compared to the other tested optimization algorithms.

scholarly journals Suspicious Activity Recognition Using Proposed Deep L4-Branched-ActionNet with Entropy Coded Ant Colony System Optimization

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Tanzila Saba ◽  
Amjad Rehman ◽  
Rabia Latif ◽  
Suliman Mohamed Fati ◽  
Mudassar Raza ◽  
...  
Keyword(s):  
Activity Recognition ◽  
System Optimization ◽  
Ant Colony ◽  
Ant Colony System ◽  
Suspicious Activity
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