A NEW PERSPECTIVE OF METAHEURISTIC ALGORITHMS

Authors

  • Pawan Shivan Othman Department of Computer Science, Nawroz University, Duhok, KRG -Iraq
  • Rasheed Rebar Ihsan Department of Computer Engineering and Communication, Nawroz University, Duhok, KRG -Iraq
  • Reving Masoud Abdulhakeem Department of Computer Science, Nawroz University, Duhok, KRG -Iraq

DOI:

https://doi.org/10.25007/ajnu.v12n1a1660

Keywords:

Optimization, Metaheuristics, Local search, Evolutionary Algorithms, Generating New Solutions

Abstract

Optimization is the art of modeling in order to produce the optimal outcome under the given conditions. The objective of optimization is to maximize or decrease the consequences that best satisfy technological and management procedures. In view of the findings, this paper provides a brief survey of methods for examining the optimization problem space, illustrates the mechanics of metaheuristic and developmental calculations, and defines their connection to constructing optimization problems. In addition to covering the encoding of metaheuristic and developmental calculations and the management of constraints, this paper also delves into the periods of introductory or provisional arrangements, the iterative determination of arrangements, and the assessment of the execution of metaheuristic and developmental calculations. All meta-heuristic and developmental calculations are shown to share a single calculation with their respective phases highlighted.

Downloads

Download data is not yet available.

References

Saka, M. P., Hasan.ebi, O., and Geem, Z. W. (2016). “Metaheuristics in structural optimization and discussions on harmony search algorithm.” Swarm and Evolutionary Computation, 28, 88–97.

S.rensen, K. (2013). “Metaheuristics: The metaphor exposed.” International Transaction in Operational Research, 22(1), 3–18.

Dokeroglu, T.; Sevinc, E.; Kucukyilmaz, T.; Cosar, A. A survey on new generation metaheuristic algorithms. Comput. Ind. Eng. 2019, 137, 106040.

Kondamadugula, S.; Naidu, S.R. Accelerated evolutionary algorithms with parameter importance based population initialization for variation-aware analog yield optimization. In Proceedings of the 2016 IEEE 59th International Midwest Symposium on Circuits and Systems (MWSCAS), Abu Dhabi, United Arab Emirates, 16–19 October 2016.

Battiti R, Brunato M, Mascia F (2008) Reactive search and intelligent optimization. Springer, Berlin

Eiben A, van der Hauw J (1997) Solving 3-sat with adaptive genetic algorithms. In: Proceedings of the fourth IEEE conference on evolutionary computation. IEEE Press, pp 81–86

Eiben AE, Smith JE (2003) Introduction to evolutionary computing. Springer, Berlin

Gent IP, Walsh T (1993) Towards an understanding of hill-climbing procedures for SAT. Proceedings of AAAI- 93:28–33

Glover F, Laguna M (1993) Tabu search. In: Reeves C (ed) Modern heuristics techniques for combinatorial problems. Blackwell Scientific Publishing, Oxford, pp 70–141

Larranaga P, Lozano JA (2001) Estimation of distribution algorithms. A new tool for evolutionary computation. Kluwer Academic Publishers, Boston

Marchiori E, Rossi C (1999) A flipping genetic algorithm for hard 3-sat problems. Proceedings of the Genetic and Evolutionary Computation Conference 1:393–400

Published

2023-02-22

How to Cite

Shivan Othman, P., Rebar Ihsan, R., & Masoud Abdulhakeem, R. . (2023). A NEW PERSPECTIVE OF METAHEURISTIC ALGORITHMS . Academic Journal of Nawroz University, 12(1), 137–142. https://doi.org/10.25007/ajnu.v12n1a1660

Issue

Section

Review Articles