scholarly journals Modified Ant Colony Optimization Algorithm for Travelling Salesman Problem

2014 ◽  
Vol 97 (10) ◽  
pp. 20-24
Author(s):  
Rohit Chaturvedi ◽  
Haider Banka
2013 ◽  
Vol 765-767 ◽  
pp. 985-988
Author(s):  
Long Xi Xu ◽  
Shan Ping Qiao

Ant colony optimization algorithm is a very important one among swarm intelligence algorithms. Because Java is an advanced object-oriented and platform-independent computer programming language, in order to use this algorithm in a platform-independent and flexible way, this paper introduces a Java-based implementation package of it. This package includes some sub-packages. There are several classes which are all implemented in Java using object-oriented technology in each sub-package. Users can utilize these classes on computers installed the corresponding Java runtime environment to solve some problems. After the test on two travelling salesman problems, these classes performed properly and efficiently, and the good effect was received.


2020 ◽  
Vol 26 (11) ◽  
pp. 2427-2447
Author(s):  
S.N. Yashin ◽  
E.V. Koshelev ◽  
S.A. Borisov

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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