Page 133 - IJOCTA-15-2
P. 133

¨
                                E. Sonu¸c, E. Ozcan / IJOCTA, Vol.15, No.2, pp.311-329 (2025)

              6. Talbi EG. Metaheuristics: From Design to Imple-  18. Yuan B, Zhang C, Shao X. A late acceptance hill-
                mentation. John Wiley & Sons. 2009.               climbing algorithm for balancing two-sided as-
                https://doi.org/10.1002/9780470496916             sembly lines with multiple constraints. J Intell
              7. Al-Betar MA. β-hill climbing: an exploratory lo-  Manuf. 2015;26:159-168.
                cal search. Neural Comput Appl. 2017;28(Suppl     https://doi.org/10.1007/s10845-013-0770-x
                1):153-168.                                   19. Fonseca GH, Santos HG, Carrano EG. Late ac-
                https://doi.org/10.1007/s00521-016-2328-2         ceptance hill-climbing for high school timetabling.
                                                                  J Sched. 2016;19:453-465.
              8. Burke EK, Bykov Y. The late acceptance
                                                                  https://doi.org/10.1007/s10951-015-0458-5
                hill-climbing heuristic.  Eur J Oper Res.
                2017;258(1):70-78.                            20. Bolaji AL A, Bamigbola AF, Shola PB. Late
                https://doi.org/10.1016/j.ejor.2016.07.012        acceptance hill climbing algorithm for solving
                                                                  patient admission scheduling problem.  Knowl-
              9. Tari S, Basseur M, Go¨effon A. Expansion-based
                                                                  Based Syst. 2018;145:197-206.
                Hill-climbing. Inf Sci. 2023;649, 119635.
                                                                  https://doi.org/10.1016/j.knosys.2018.01.017
                https://doi.org/10.1016/j.ins.2023.119635
                                                              21. Erlenkotter D. A dual-based procedure for
             10. Burke EK, Bykov Y. The late acceptance hill-
                                                                  uncapacitated facility location.  Oper Res.
                climbing heuristic. University of Stirling, Tech.
                                                                  1978;26(6):992-1009.
                Rep. 2012.
                                                                  https://doi.org/10.1287/opre.26.6.992
             11. Alparslan S, Sonu¸c, E.. Solving Static Weapon-
                                                              22. Efroymson  M,   Ray   TL.  A   branch-bound
                Target Assignment Problem using Multi-Start
                                                                  algorithm  for  plant  location.  Oper Res.
                Late Acceptance Hill Climbing.  Curr Trends
                                                                  1966;14(3):361-368.
                Comput Sci Appl. 2024;2(1):23-35.
                                                                  https://doi.org/10.1287/opre.14.3.361
             12. Bazargani M, Lobo FG. Parameter-less late ac-  23. Akinc U, Khumawala BM. An efficient branch
                ceptance hill-climbing. In:  Proceedings of the   and  bound   algorithm  for  the  capacitated
                Genetic and Evolutionary Computation Confer-      warehouse  location  problem.   Manag Sci.
                ence. 2017; 219-226.                              1977;23(6):585-594.
                https://doi.org/10.1145/3071178.3071225           https://doi.org/10.1287/mnsc.23.6.585
             13. Goerler A, Schulte F, Voß, S. An application  24. Schrage L. Implicit representation of variable up-
                of late acceptance hill-climbing to the traveling  per bounds in linear programming. In: Compu-
                purchaser problem. In: Computational Logistics:   tational practice in mathematical programming.
                4th International Conference ICCL 2013, Copen-    2009; 118-132. Springer.
                hagen, Denmark, September 25-27, 2013. Pro-       https://doi.org/10.1007/BFb0120715
                ceedings 4 2013; 173-183. Springer.
                                                              25. Galv˜ao RD, Raggi LA. A method for solving to
                https://doi.org/10.1007/978-3-642-41019-2 1 3
                                                                  optimality uncapacitated location problems. Ann
             14. Ghosh M, Kundu T, Ghosh D, Sarkar R. Feature     Oper Res. 1989;18(1):225-244.
                selection for facial emotion recognition using late  https://doi.org/10.1007/BF02097805
                hill-climbing based memetic algorithm. Multimed  26. Hoefer M. Experimental comparison of heuristic
                Tools Appl. 2019;78, 25753-25779.                 and approximation algorithms for uncapacitated
                https://doi.org/10.1007/s11042-019-07811-x        facility location. In: International Workshop on
             15. Turky A, Sabar NR, Sattar A, Song A. Paral-      Experimental and Efficient Algorithms. 2003; 165-
                lel late acceptance hill-climbing algorithm for the  178. Springer.
                google machine reassignment problem. In: AI       https://doi.org/10.1007/3-540-44867-5 1 3
                2016: Advances in Artificial Intelligence: 29th  27. Monabbati E, Kakhki HT. On a class of subaddi-
                Australasian Joint Conference Hobart, TAS, Aus-   tive duals for the uncapacitated facility location
                tralia, December 5-8, 2016, Proceedings 29 2016;  problem. Appl Math 2015;251:118-131.
                163-174. Springer International Publishing.       https://doi.org/10.1016/j.amc.2014.10.072
                https://doi.org/10.1007/978-3-319-50127-7 1 3  28. Sonu¸c, E.. Binary crow search algorithm for the
             16. Clay S, Mousin L, Veerapen N, Jourdan L. Clahc-  uncapacitated facility location problem. Neural
                custom late acceptance hill climbing: First re-   Comput Appl. 2021;33(21):14669-14685.
                sults on tsp. In: Proceedings of the Genetic and  https://doi.org/10.1007/s00521-021-06107-2
                Evolutionary Computation Conference Compan-   29. Durgut R, Aydin ME. Adaptive binary artifi-
                ion. 2021; 1970-1973.                             cial bee colony algorithm.  Appl Soft Comput.
                https://doi.org/10.1145/3449726.3463129           2021;101:107054.
             17. Cao VL, Nicolau M, McDermott J. Late-            https://doi.org/10.1016/j.asoc.2020.107054
                                                                             ¨
                acceptance and step-counting hill-climbing GP for  30. Sonu¸c, E., Ozcan E. An adaptive parallel evo-
                anomaly detection. In: Proceedings of the Genetic  lutionary algorithm for solving the uncapaci-
                and Evolutionary Computation Conference Com-      tated facility location problem. Expert Syst Appl.
                panion. 2017; 221-222.                            2023;224:119956.
                https://doi.org/10.1145/3067695.3076091           https://doi.org/10.1016/j.eswa.2023.119956
                                                           328
   128   129   130   131   132   133   134   135   136   137   138