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

Nicolás Rojas-Morales, María Cristina Riff, Elizabeth Montero

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish
Article number107341
JournalKnowledge-Based Systems
Publication statusPublished - 11 Oct 2021


  • Ant colony optimization
  • Constraint Satisfaction Problems
  • Opposition-Inspired Learning

ASJC Scopus subject areas

  • Management Information Systems
  • Software
  • Information Systems and Management
  • Artificial Intelligence


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