Memetic algorithm for solving multi-objective assignment problem

Document Type : Research Paper


Department of Mathematics, University of Baghdad, Baghdad 00964, Iraq


Despite the fact that statistical solutions for dealing with Multi-Objective Assignment Problems (MOAP) have just been available for a long time, the further application of Evolutionary Algorithms (EAs) to such difficulties presents a vehicle for tackling MOAP with an extraordinarily large scope. MOGASA is a suggested multi-objective optimizer with simulated annealing that combines the hegemony notion with a discrete wavelet transform. While decomposition streamlines the multi-objective problem (MOP) by expressing it as a collection of many corresponding authors, tackling these issues at the same time in the GA context may result in early agreement due to the command meanwhile screening process, which employs the Methodology as a criterion. Supremacy is important in constructing the leaders archive because it allows the chosen leaders to encompass fewer dense regions, eliminating local minima and meanwhile producing a more diverse approximating Allocative efficiency front. MOGASA outperforms several decomposition-based growth strategies, according to results from 31 stand meanwhile are MOPs. MATLAB was used to generate all of the findings (R2017b).


[1] C. Adiche and M. Aider, A hybrid method for solving the multi-objective assignment problem, J. Math. Model. Algor. 9 (2010), no. 2, 149–164.
[2] M. Ayob, S. Abdullah and A.M.A. Malik, A practical examination timetabling problem at the Universiti Kebangsaan Malaysia, Int. J. Comput. Sci. Network Secur. 9 (2007), no. 7, 198–204.
[3] H. Basirzadeh, Ones assignment method for solving assignment problems, Appl. Math. Sci. 6 (2012), no. 45-48, 2345-2355.
[4] M. Caramia, P. Dell’Olmo and G.F. Italiano, Novel local-search-based approaches to university examination timetabling, INFORMS J. Comput. 20 (2008), no. 1, 86–99.
[5] C.A.C. Coello, G.B. Lamont and D.A. Van. Veldhuizen evolutionary algorithms for solving multi-objective problems, New York, Springer, 2007.
[6] E. Ersoy, E. ¨ Ozcan and A. S. Uyar, Memetic algorithms and hyperhill-climbers, Proc. 3rd Multidiscip. Int. Schedul. Conf.: Theory Appl. (MISTA07), 2007, pp. 159–166.
[7] A.M.K. Hammadi, Application of linear programming according to the allocation model, Al-Muthanna J.  Admin. Econ. Sci. 2 (2012), no. 3.
[8] A.M.K. Hammadi, Solving multi objective Assignment problem using Tabu search algorithm, Glob. J. Pure Appl. Math. 13 (2017), no. 9, 4747–4764.
[9] A.A. Humaidan, A model of linear programming for the movement of pilgrims from Arafat to Muzdalifah, Damascus J. Econ. Legal Sci. 21 (2005), no. 21.
[10] S. Mirjalili, S. Saremi, S.M. Mirjalili and L.D.S. Coelho, Multi-objective grey wolf optimizer: a novel algorithm for multi-criterion optimization, Expert Syst. Appl. 47 (2016), 106–119.
[11] S. Mardle, S. Pascoe, and M. Tamiz, An investigation of genetic algorithms for the optimization of multi-objective fisheries bio-economic models, Proc. Third Int. Conf. Multi-Objective Program. Goal program. Theory Appl., Quebec City, 1998.
[12] R. Qu, E.K. Burke, B. McCollum, L.T.G. Merlot, and S.Y. Lee, A survey of search methodologies and automated system development for examination timetabling, J. Schedul. 12 (2009), no. 1, 55–89.
[13] D.H. Rogers, M. Alam and L.K. Shaw, Kansas Irrigation Trends, Agricultural Experiment Station and Cooperative Extension Service, Kansas State University, 2008.
[14] K. Salehi, An approach for solving multi-objective assignment problem with interval parameters, Manag. Sci. Lett. 4 (2014), no. 9, 2155–2160.
[15] R.H. Sheah and I.T. Abbas, Using multi-objective bat algorithm for solving multi-objective non-linear programming problem, Iraqi J. Sci. 62 (2021), no. 3, 997–1015.
[16] S. Singh, A comparative analysis of assignment problem, IOSR J. Engin. 8 (2012), no. 2, 1–15.
[17] H. Turabieh and S. Abdullah, An integrated hybrid approach to the examination timetabling problem, Omega 39 (2011), no. 6, 598–607.
[18] E.L. Ulungu and J. Teghem, Multi-objective combinatorial optimization problems: A survey, J. Multi-Criteria Decision Anal. 3 (1994), no. 2, 83–104.
[19] G. Yue, M. Chen and H. Ishii, Bi-criteria bottleneck assignment problem, Fuzzy Inf. Process. Soc. 2012 Annual Meeting of the North American. IEEE, 2012.
Volume 14, Issue 7
July 2023
Pages 73-80
  • Receive Date: 01 March 2022
  • Revise Date: 17 April 2022
  • Accept Date: 13 June 2022
  • First Publish Date: 01 October 2022