A review of exploring recent advances in ant ‎colony optimization: applications and ‎improvements

  • Authors

    • Renjbar Sh. Othman MSc student
    • Ibrahim Mahmood Ibrahim Akre University for Applied Sciences, Technical College of Informatics-Akre, Department of Computer Network and Information Security, Kurdistan Region, Iraq.

    Received date: February 19, 2025

    Accepted date: March 6, 2025

    Published date: March 20, 2025

    https://doi.org/10.14419/s0sjgq84
  • Ant Colony Optimization; Metaheuristics; Path Planning; Hybrid Algorithms; Optimization ‎Techniques

  • Abstract

    Inspired by the foraging behavior of ants, the well-known metaheuristic Ant Colony Optimization ‎‎(ACO) provides strong answers to challenging optimization issues in many spheres. This work ‎investigates current developments in ACO algorithms with an emphasis on hybridization, employing methods including machine learning, adaptive mechanisms, and genetic algorithms to ‎improve performance. Applications such as robotics, telecommunications, healthcare, and logistics ‎show ACO's adaptability in handling path planning, resource allocation, and data optimization. ‎Dynamic pheromone methods, multi-objective optimization, and domain-specific adaptations ‎, which have raised computing efficiency, scalability, and solution quality, have been key advances. ‎Notwithstanding these developments, problems, including parameter sensitivity and real-time ‎adaptation, remain unresolved. Future studies include integrating real-time data, creating scalable ‎adaptive algorithms, and tackling domain-specific restrictions to further increase ACO's relevance. ‎This work emphasizes ACO's possible importance as a fundamental instrument for addressing ‎problems of real-world optimization‎.

     

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    Sh. Othman, R., & Ibrahim Mahmood Ibrahim. (2025). A review of exploring recent advances in ant ‎colony optimization: applications and ‎improvements. International Journal of Scientific World, 11(1), 114-122. https://doi.org/10.14419/s0sjgq84

    Received date: February 19, 2025

    Accepted date: March 6, 2025

    Published date: March 20, 2025