In this study, a hybrid ant colony optimization (HACO) method is proposed, whose major goal is to combine the advantages of the AS, ACS, and MMAS previously put out by various scholars at various points in time. Using novel Transition Probability relations with a Jump transition probability relation, the HACO method is used in this paper to solve optimization issues. This approach also identifies the point or path where the desired optimal value has been reached. Additionally, it offers a new pheromone updating rule and a pheromone evaporation residue that determines how much pheromone is still present after updating. This serves as a map for the next ant travelling the trail and other local search techniques. Given that the HACO algorithm has been tested on a variety of combinatorial optimization problems and the results have been proved to favourably compare with analytical results, we note that its computational efficiency quickly uncovers very workable solutions.
Author(s) Details:
Kayode James Adebayo,
Department of Mathematics, Ekiti State University, Ado Ekiti, Ekiti State, Nigeria.
Felix Makanjuola Aderibigbe,
Department of Mathematics, Ekiti State University, Ado Ekiti, Ekiti State, Nigeria.
Adejoke Olumide Dele-Rotimi,
Bamidele Olumilua University of Education, Science and Technology, Ikere Ekiti, Ekiti State, Nigeria.
Please see the link here: https://stm.bookpi.org/NRAMCS-V8/article/view/8356
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