Bio-inspired solutions are often applied to solve optimization problems. In this paper the introduction of chaotic systems in Ant Colony Optimization (ACO) algorithms is investigated. The ACO strategy is inspired by the cooperative behavior of food retrieval shown by ants that collectively discover the shortest path between ant colony and food sources. The optimization problem examined in this work is the well-known Travelling Salesman Problem (TSP), a standard test bench for new combinatory optimization algorithms. The simulation results show that the application of deterministic chaotic signals instead of random sequences is a possible strategy to improve the performances of ACO algorithms.

Chaotic sequences in ACO algorithms

Patane, L
2004

Abstract

Bio-inspired solutions are often applied to solve optimization problems. In this paper the introduction of chaotic systems in Ant Colony Optimization (ACO) algorithms is investigated. The ACO strategy is inspired by the cooperative behavior of food retrieval shown by ants that collectively discover the shortest path between ant colony and food sources. The optimization problem examined in this work is the well-known Travelling Salesman Problem (TSP), a standard test bench for new combinatory optimization algorithms. The simulation results show that the application of deterministic chaotic signals instead of random sequences is a possible strategy to improve the performances of ACO algorithms.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11570/3150846
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