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Reseach Article

Memetic Algorithm for Dynamic Optimization Problems

by AL-Khafaji Amen
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 136 - Number 3
Year of Publication: 2016
Authors: AL-Khafaji Amen
10.5120/ijca2016908393

AL-Khafaji Amen . Memetic Algorithm for Dynamic Optimization Problems. International Journal of Computer Applications. 136, 3 ( February 2016), 7-10. DOI=10.5120/ijca2016908393

@article{ 10.5120/ijca2016908393,
author = { AL-Khafaji Amen },
title = { Memetic Algorithm for Dynamic Optimization Problems },
journal = { International Journal of Computer Applications },
issue_date = { February 2016 },
volume = { 136 },
number = { 3 },
month = { February },
year = { 2016 },
issn = { 0975-8887 },
pages = { 7-10 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume136/number3/24131-2016908393/ },
doi = { 10.5120/ijca2016908393 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:36:00.832939+05:30
%A AL-Khafaji Amen
%T Memetic Algorithm for Dynamic Optimization Problems
%J International Journal of Computer Applications
%@ 0975-8887
%V 136
%N 3
%P 7-10
%D 2016
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Dynamic optimization problems has attracted much attention of researchers. However, due to complexity and uncertainty to solve dynamic optimization problems, it very difficult to find out the optimum solution that could be changed over time. Thus, it is necessary to develop efficient or improved an algorithms to solve dynamic optimization problems. A memetic algorithm that based on local search along with an evolutionary algorithm such as genetic algorithm can be used to tackle dynamic optimization problems. This paper investigates the use of multi-crossover operator that is based on heuristic and arithmetic with GA as well as local search for dynamic optimization problems. The proposed approach utilises solution features in terms of diversity and selection to generate better solution. To evaluate the efficiency and feasibility of the proposed operator, a comparison between the results of this study and the results of different works is conducted through a number of evaluations over dynamic optimization problems with various levels of difficulty. The significant findings emerge from this study are the efficiency of the proposed algorithm in solving dynamic environments when compared with other method.

References
  1. Wang, H., D. Wang, and S. Yang, A memetic algorithm with adaptive hill climbing strategy for dynamic optimization problems. Soft Computing, 2009. 13(8-9): p. 763-780.
  2. Turky, A.M. and S. Abdullah, A multi-population harmony search algorithm with external archive for dynamic optimization problems. Information Sciences, 2014. 272: p. 84-95.
  3. Turky, A.M., S. Abdullah, and N.R. Sabar, A Hybrid Harmony Search Algorithm for Solving Dynamic Optimisation Problems. Procedia Computer Science, 2014. 29: p. 1926-1936.
  4. Turky, A.M. and S. Abdullah, A multi-population electromagnetic algorithm for dynamic optimisation problems. Applied Soft Computing, 2014. 22: p. 474-482.
  5. Turky, A.M., S. Abdullah, and N.R. Sabar, Meta-heuristic algorithm for binary dynamic optimisation problems and its relevancy to timetabling, in 10th international conference on the Practice and Theory of Automated Timetabling (PATAT 2014), pp. 568-573. 26-29 August 2014, York, UK.
  6. Turky, A.M., et al., An Evolutionary Hill Climbing Algorithm for Dynamic Optimisation Problems, in The 6th Multidisciplinary Int. conf. On Scheduling: Theory and Applications (MISTA 2013), pp. 795-798. Ghent, Belgium (27-30 Aug 2013).
  7. Yang, S. Memory-based immigrants for genetic algorithms in dynamic environments. in Proceedings of the 7th annual conference on Genetic and evolutionary computation. 2005. ACM.
  8. Talbi, E.-G., A taxonomy of hybrid metaheuristics. Journal of heuristics, 2002. 8(5): p. 541-564.
  9. Yang, S. and X. Yao, Experimental study on population-based incremental learning algorithms for dynamic optimization problems. Soft Computing, 2005. 9(11): p. 815-834.
  10. Yang, S. Non-stationary problem optimization using the primal-dual genetic algorithm. in Evolutionary Computation, 2003. CEC'03. The 2003 Congress on. 2003. IEEE.
  11. Cobb, H.G., An investigation into the use of hypermutation as an adaptive operator in genetic algorithms having continuous, time-dependent nonstationary environments, 1990, DTIC Document.
  12. Branke, J., et al., A multi-population approach to dynamic optimization problems, in Evolutionary Design and Manufacture2000, Springer. p. 299-307.
  13. Yang, S., Genetic algorithms with elitism-based immigrants for changing optimization problems, in Applications of Evolutionary Computing2007, Springer. p. 627-636.
  14. Grefenstette, J.J. Genetic algorithms for changing environments. in PPSN. 1992.
  15. Yang, S., Genetic algorithms with memory-and elitism-based immigrants in dynamic environments. Evolutionary Computation, 2008. 16(3): p. 385-416.
Index Terms

Computer Science
Information Sciences

Keywords

Arithmetic Crossover Operator Dynamic optimization problems Evolutionary algorithms Genetic algorithm Heuristic crossover Local search Mutation Operators Memetic algorithm.