 Finally, which meta-heuristic algorithm is the best one?Elham Shadkam, Samane Safari and Saba Saberi Abdollahzadeh International Journal of Decision Sciences, Risk and Management, 2021, vol. 10, issue 1/2, 32-50 Abstract: This paper is conducted to rank the six meta-heuristic algorithms such as the genetic, ant colony optimisation, tabu search, particle swarm optimisation, imperialist competitive, and simulated annealing algorithm and choose the most efficient algorithm among them, concerning collected information. Accordingly, three multi-criteria decision-making methods, including the analytical hierarchy process (AHP), TOPSIS, and AHP-TOPSIS methods are used. Criteria for comparing algorithms are selected based on the capability of each algorithm and the issues that are necessary to solve each problem. The result of the TOPSIS method indicates the superiority and efficiency of the tabu search algorithm. However, the analytical hierarchy process presents the ant colony algorithm as the best algorithm. Also, in the AHP-TOPSIS method, the best meta-heuristic algorithm is genetic. Finally, according to the results obtained from all three methods and the use of the combined compromise solution method (CoCoSo), the genetic algorithm is selected as the best algorithm. Keywords: meta-heuristic algorithms; TOPSIS; analytical hierarchy process; AHP; multi-criteria decision-making; combined compromise solution method; CoCoSo. (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:ids:ijdsrm:v:10:y:2021:i:1/2:p:32-50 Access Statistics for this article More articles in International Journal of Decision Sciences, Risk and Management from Inderscience Enterprises Ltd |
Is your work missing from RePEc? Here is how to contribute.
Questions or problems? Check the EconPapers FAQ or send mail to .
EconPapers is hosted by the
School of Business at Örebro University.