Determination of Fuzzy Logic Membership Functions Using Extended Ant Colony Optimization Algorithm

2008 ◽  
Author(s):  
Huan Jiang ◽  
Huiwen Deng ◽  
Yingsi He
Keyword(s):  
Fuzzy Logic ◽  
Ant Colony Optimization ◽  
Optimization Algorithm ◽  
Ant Colony ◽  
Membership Functions ◽  
Ant Colony Optimization Algorithm

Determination of the Heat Flux in the Process of Solidification by Applying the Ant Colony Optimization Algorithm

2014 ◽  
Vol 622-623 ◽  
pp. 764-771
Author(s):  
Edyta Hetmaniok ◽  
Damian Slota
Keyword(s):  
Heat Flux ◽  
Ant Colony Optimization ◽  
Optimization Algorithm ◽  
Ant Colony ◽  
Second Step ◽  
Ant Colony Optimization Algorithm ◽  
Solidification Problem ◽  
The Finite Difference Method ◽  
Phase Truncation

The paper presents an application of Ant Colony Optimization algorithm as a partof procedure for solving the inverse solidification problem. Investigated task consists in recon-struction of the boundary condition (heat flux) on the basis of temperature measurements inselected points of the cast. First step of the method is based on the finite difference method withapplication of the generalized alternating phase truncation method and serves for solving theappropriate direct solidification problem. In the second step some functional representing thecrucial part of the procedure is minimized with the aid of Ant Colony Optimization algorithm.

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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