Extended experiments with Ant Colony Optimization with heterogeneous ants for Large Dynamic Traveling Salesperson Problems



In this work we study the ACS with heteroge- neous ants approach to big dynamic problems. When building solutions ACO algorithms rely in two sources of information: static heuristic information about the instance being solved, and dynamic trail information acquired during the execution. Conventional ACS always use both sources of informations; ACS with restart clears the trail each time a change occurs and so, immediately after each change, it relies solely on the heuristic information; the heterogeneous ants, or multi-caste approach, as implemented for this work, have the ability to either use both sources of information or none. We compare the performance of various variants and configurations against both the conventional ACS and conventional ACS with restart, and analize the strengths and weaknesses of each when applied to a set of instances of and dynamic scenarios.


ant colony optimization; dynamic optimization problems; heterogeneous ants; traveling salesperson problem


Ant Colony Optimization


ICCSA14: The 14th International Conference on Computational Science and Its Applications, June 2014


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Year 2018 : 2 citations

 Mavrovouniotis, Michalis; Shengxiang Yang, : Ant colony optimization for dynamic combinatorial optimization problems (Control, Robotics & Sensors, 2018), 'Swarm Intelligence - Volume 1: Principles, current algorithms and methods', Chap. 5, pp. 121-142, DOI: 10.1049/PBCE119F_ch5.

 M. Mavrovouniotis, S. Yang, Ant Colony Optimization for Dynamic Combinatorial Optimization Problems, Swarm Intelligence: From Concepts to Applications, chapter 5, Publisher The IET, 2018

Year 2017 : 2 citations

 A. Fayeez, E. Keedwell, M. Collett. H-ACO: A Heterogeneous Ant Colony Optimisation approach with Application to the Travelling Salesman Problem, Proceeding of EA 2017.

 M. Mavrovouniotis, C. Li, S. Yang, A survey of swarm intelligence for dynamic optimization: Algorithms and applications, Swarm and Evolutionary Computation 33:1-17, April 2017