M. Fatih TasgetirenQuanke PanÖnder BulutPonnuthurai Nagaratnam SuganthanTasgetiren, M. FatihSuganthan, P. N.Pan, Quan-KeBulut, OnderFadiloglu, M. Murat2025-10-06201197816128407272330-237210.1109/SDE.2011.59520622-s2.0-79961160441https://www.scopus.com/inward/record.uri?eid=2-s2.0-79961160441&doi=10.1109%2FSDE.2011.5952062&partnerID=40&md5=77e78b2bad7c1c0cd2f06158818be0fdhttps://gcris.yasar.edu.tr/handle/123456789/10228https://doi.org/10.1109/SDE.2011.5952062This paper extends the applications of differential evolution algorithms to the Median Cycle Problem. The median cycle problem is concerned with constructing a simple cycle composed of a subset of vertices of a mixed graph. The objective is to minimize the cost of the cycle and the cost of assigning vertices not on the cycle to the nearest vertex on the cycle. A unique solution representation is presented for the differential evolution algorithm in order to solve the median cycle problem. To the best of our knowledge this is the first reported application of differential evolution algorithms to the median cycle problem in the literature. No local search is employed in order to see the performance of the pure differential evolution algorithm. The differential evolution algorithm is tested on a set of benchmark instances from the literature. For comparisons a continuous genetic algorithm is also developed. The computational results show that the differential evolution algorithm was superior to the genetic algorithm. In addition the computational results also show that the differential evolution algorithm is very promising in solving the median cycle problem when compared to the best performing algorithms from the literature. Ultimately given the fact that no local search is employed the DE algorithm was able to further improve the 5 out of 20 instances. © 2011 IEEE. © 2011 Elsevier B.V. All rights reserved.Englishinfo:eu-repo/semantics/closedAccessDifferential Evolution, Heuristic Optimization, Median Cycle Problem, Random Key Evolutionary Algorithm, Computational Results, Continuous Genetic Algorithms, De Algorithms, Differential Evolution, Differential Evolution Algorithms, Heuristic Optimization, Local Search, Median Cycle Problem, Mixed Graph, Random Key Evolutionary Algorithm, Solution Representation, Artificial Intelligence, Genetic Algorithms, BiologyComputational results, Continuous genetic algorithms, DE algorithms, Differential Evolution, Differential evolution algorithms, Heuristic optimization, Local search, Median cycle problem, Mixed graph, random key evolutionary algorithm, Solution representation, Artificial intelligence, Genetic algorithms, BiologyDifferential EvolutionEconomic Lot SchedulingRandom Key Evolutionary AlgorithmHeuristic OptimizationMedian Cycle ProblemConference Object