Opposition-Inspired synergy in sub-colonies of ants: The case of Focused Ant Solver

Nicolás Rojas-Morales, María Cristina Riff, Elizabeth Montero

Resultado de la investigación: Contribución a una revistaArtículorevisión exhaustiva


Recently, Opposition-Inspired Learning strategies were proposed to improve the search process of ant-based algorithms to solve combinatorial problems. In this paper, we propose a collaborative framework of these strategies called Multiple Opposite Synergic Strategy for Ants (MOSSA). Because of each strategy has a different goal, we expect that the ants algorithm will benefit from their collaboration. The algorithm strongly uses the pheromone matrix for accomplishing stigmergy. To evaluate our framework, we use a recently proposed algorithm to solve Constraint Satisfaction Problems named Focused Ant Solver. Results and statistical analysis show that using MOSSA, Focused Ant Solver is able to solve more problems from the transition phase.

Idioma originalInglés
Número de artículo107341
PublicaciónKnowledge-Based Systems
EstadoPublicada - 11 oct 2021

Áreas temáticas de ASJC Scopus

  • Sistemas de gestión de la información
  • Software
  • Gestión y sistemas de información
  • Inteligencia artificial


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