Aljanaby A.Ku-Mahamud K.R.Norwawi N.Md.2024-05-292024-05-29201097808900000002-s2.0-77954598967https://www.scopus.com/inward/record.uri?eid=2-s2.0-77954598967&partnerID=40&md5=f0a71a4101236cc23350daa6c747f9fchttps://oarep.usim.edu.my/handle/123456789/9950Interacted Multiple Ant Colonies Optimization (IMACO) is a newly proposed framework. Pheromone evaluation mechanism is playing a central role in this framework. This paper describes the newly proposed IMACO framework and proposes a more effective pheromone evaluation mechanism. Computational tests show that the new pheromone evaluation mechanism can furthermore improve the IMACO performance. These tests also show the capability of IMACO to outperform other well known ant algorithms like ant colony system and max-min ant system.en-USAnt colony optimizationCombinatorial optimization problemsPheromone evaluationSearch stagnationAnt algorithmsAnt coloniesAnt colony systemsAnt-colony optimizationCombinatorial optimization problemsComputational testsMax-Min Ant SystemOptimization frameworkAlgorithmsArtificial intelligenceCombinatorial optimizationConference Paper1215