Generalized Net Model for Flying Ant Colony Optimization View Full Text


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Chapter Info

DATE

2021-04-04

AUTHORS

Stefka Fidanova , Krassimir Atanassov

ABSTRACT

Ant Colony Optimization (ACO) has been used successfully to solve hard combinatorial optimization problems. The method is inspired by the foraging behavior of real ant colonies, which manage to establish the shortest routes to feeding sources and back. In ACO the problem is represented as a graph and our artificial ants look for a shorter path taking in to account some constraints. The method is constructive. Every ant creates its solution, starting from random node. It includes new node in the partial solution according probabilistic rule. In traditional ACO an ant can observe only the neighbor nodes and decides to include it or not in the partial solution. In our previous work we propose ACO with flying ants. The ants make a decision regarding more than one step forward, they observe the neighbors of the neighbors too. Thus they can make better decision.The Generalized Nets (GN) is a powerful tool for modeling real processes. In this work we apply GN for modeling Flying Ant Colony Optimization (FACO). Description of FACO by GN can give us new ideas for further development and improvement of the algorithm. More... »

PAGES

90-98

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-3-030-71616-5_10

DOI

http://dx.doi.org/10.1007/978-3-030-71616-5_10

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1136907732


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