On Tackling Real-Life Optimization Problems

Nadia Abd-Alsabour


Most real-world applications are concerned with minimizing or maximizing some quantity so as to enhance some result. This emphasizes the importance of optimization and subsequently the significance of the optimization methods that are able to tackle these real-life optimization problems. There are a number of practical reasons for which traditional optimization and exhaustive algorithms cannot deal with a variety of these real-life optimization applications although there are numerous optimization problems that can benefit from applying these traditional optimization algorithms to handle them. Therefore, their is a need for propsong new optimization algorithms (such as nature inspired optimization methods) and optimize the capabilities of the existing ones (such as hybridization and parallelization) as well. This paper investigates the most recent optimization directions for dealing with the real-life optimization problems with an application to one of the most common and important optimization problems in a variety of financial fields and other fields which is the portfolio optimization problem since it is considered one of the most crucial problems in the modern financial management and has a variety of applications such as asset management and building strategic asset allocation. The computational results were got utilizing benchmark data from the OR library with the use of modern optimization algorithms. In addition, the article highlights the differences and similarities among the utilized optimization methods. In addition, recent advancements to the utilized optimization methods are highlighted.


real-world problems; nature-inspired algorithms; differential evolution (DE); particle swarm optimization (PSO).

Full Text:



X. Yang, "Nature-Inspired Optimization Algorithms: A Tutorial", IDEAL, 2018.

I. C. Obagbuwa, "Swarm Intelligence Algorithms and Applications to Real-world Optimization Problems: A Survey", International Journal of Simulation, Systems, Science & Technology, vol. 19, no. 2, pp.1-8. 8, 2018.

K. Lwin, R. Qu, "A hybrid algorithm for constrained portfolio selection problems", Applied intelligence, Kluwer, 2013.

W. J. Gutjahr, "A generalized convergence result for the graph-based ant system metaheuristic", Probability in the Engineering and Informational Sciences, vol. 17, no. 4, pp. 545-569, 2003.

N. Bacanin, and M Tuba, "Firefly Algorithm for Cardinality Constrained Mean-Variance Portfolio Optimization Problem with Entropy Diversity Constraint", The Scientific World Journal, vol. 2014, Article ID 721521, 16 pages, 2014.

W. Fang, X. Li, M. Zhang, and M. Hu, "Nature-Inspired Algorithms for Real-World Optimization Problems", Journal of Applied Mathematics, vol. 2015, Article ID 359203, 2 pages, 2015.

N. Siddique and H. Adeli, "Nature Inspired Computing: An Overview and Some Future Directions", Cognitive Computing, vol. 7, pp. 706–714, 2015.

G. Tollo, and A. Roli, "Metaheuristics for the Portfolio Selection Problem", International Journal of Operations Research, vol. 5, no. 1, pp. 13-35, 2008.

J.N. Kapiamba, E.L.B. Ulungu, and P.K. Mubenga, "Simulated annealing vs. genetic algorithm to portfolio selection", International Journal of Scientific and Innovative Mathematical Research, vol. 3, pp.18-30, 2015.

A. John, A. I. Logubayom, and J. Ackora-Prah, "Portfolio Optimization Using Matrix Approach: A Case of Some Stocks on the Ghana Stock Exchange", International Journal of Accounting, Finance and Risk Management, vol. 2, no. 1, 2017.

Calculating covariance for stocks, available at: https://www.investopedia.com/.../11/calculating-covariance.asp. Last visited on 1-11-2018.

C. Blum, and A. Roli, "Metaheuristics in Combinatorial Optimization: Overview and Conceptual Comparison", ACM Computing Surveys, vol. 35, no. 3, pp. 268–308, 2003.

A. Kresta and, K. Slova, "Solving cardinality constrained portfolio optimization problem by binary particle swarm optimization algorithm", Department of Mathematical Methods in Economics, Faculty of Economics, VŠB-Technical University of Ostrava, Sokolská třída, vol.33, no. 701, 2011.

R. Moral-Escudero, R. Ruiz-Torrubiano, and A. Su´arez, “Selection of optimal investment portfolios with cardinality constraints,†in Proceedings of the 2006 Congress on Evolutionary Computation (CEC2006), 2006, pp. 2382–2388.

D. Bienstock, “Computational study of a family of mixed-integer quadratic programming problems,†Mathematical programming, vol. 74, pp. 121–140, 1996.

D. Bertsimas and R. Shioda, “Algorithm for cardinality-constrained quadratic optimization,†Computational Optimization and Applications, vol. 43, no. 1, pp. 1–22, May 2009.

A. Shukla, R. Tiwari, and R.: Kala, "Real Life Applications of Soft Computing", CRC Press, 2010.

M. Gilli, D. Maringer, and E. Schumann, "Numerical Methods and Optimization in Finance", Elsevier, 2011.

S. Fidanova, "Ant colony optimization and multiple knapsack problem", In: Rennard JP (Ed.), Handbook of research on nature inspired computing for economics and management. Idea Group, pp. 498-509, 2007.

V. Maniezzo, and M. Roffilli, "Very strongly constrained problems: an ant colony optimization approach", Cybernetics and Systems: An International Journal, vol. 39, no. 4, pp. 395-424. 2008.

V. Maniezzo, and M. Milandri, "An Ant-Based Framework for Very Strongly Constrained Problems", in Dorigo, M. et al. (Eds.): Ants 2002, LNCS, vol. 2463, pp. 222-227, Springer, 2002.

M., Dorigo, and T. Stutzle, "Ant Colony Optimization", MIT Press, Cambridge, 2004.

S. Das, SS. Mullick, P.N. Suganthan, "Recent advances in differential evolution– An updated survey", Swarm and Evolutionary Computation, vol.2, no. 7, pp. 1–30, 2016.

C.C. Li, H. Lin, and J.C. Liu, "Parallel genetic algorithms on the graphics processing units using island model and simulated annealing" Advances in Mechanical Engineering, vol. 9, no. 7, 2017, 1687814017707413.

J. Madera, E. Alba, and A. Ochoa, "A Parallel Island Model for Estimation of Distribution Algorithms", In: Jose A. Lozano, P. Larranaga, I. Inza, E. Bengoetxea (Eds.): Towards a New Evolutionary Computation. Springer-Verlag Berlin Heidelberg. 2006.

E. Cantú-Paz, "A survey of parallel genetic algorithms", Calculateurs paralleles, reseaux et systems repartis, vol. 10, no.2, pp.141-171. 1998.

G. Luque, and E. Alba, Parallel Genetic Algorithms: Theory and Real World Applications. Springer. 2011.

R. Storn and K. Price, “Differential evolution-a simple and efficient heuristic for global optimization over continuous spaces,†Journal of Global Optimization, vol. 11, no. 4, pp. 341–359, 1997.

S. Das, P.N. Suganthan, "Differential evolution: a survey of the state-of-the-art", IEEE Trans.Evol.Comput.vol.15, no. 1, pp. 4–31, 2011.

F. Neri, and V. Tirronen, "Recent advances in differential evolution: a survey and experimental analysis", Artificial Intelligence Review, vol. 33, no. 1-2, pp. 61-106, 2010.

V. Kachitvichyanukul, "Comparison of Three Evolutionary Algorithms: GA, PSO, and DE", Industrial Engineering & Management Systems, vol. 11, no. 3, pp.215-223, September 2012.

G. A. Trunfio, "A Cooperative Co-evolutionary Differential Evolution Algorithm with Adaptive Subcomponents", ICCS International Conference on Computational Science, 2015.

J. Kennedy, and R. Eberhart, Particle Swarm Optimization, proceedings of IEEE International Conference on Neural Networks, pp. 1942–1948, 1995.

C. P. Lim, and L. C. Jain, "Advances in Swarm Intelligence", In: C.P. Lim et al. (Eds.): Innovations in Swarm Intelligence, SCI, vol. 248, pp. 1–7, Springer, 2009.

J. Barrera, and C. A. Coello Coello, "A Review of Particle Swarm Optimization Methods Used for Multimodal Optimization", In: C.P. Lim et al. (Eds.): Innovations in Swarm Intelligence, SCI, vol. 248, pp. 1–7, Springer, 2009.

R. Mendes, J. Kennedy, and J. Neves, "The Fully Informed Particle Swarm: Simpler, may be Better", IEEE Transactions on Evolutionary Computation, vol. 8, pp. 204–210, 2004.

R. Wahono, and N. Suryana, "Combining PSO based Feature Selection and Bagging Technique for Software Defect Prediction", International Journal of Software Engineering and Its Applications, vol.7, no.5, pp. 153-166, 2013.

B. Lui, "Web Data Mining", Springer, 2010.

N. Abd-Alsabour, "A review on evolutionary feature selection", In Modelling Symposium (EMS), 2014 European, pp. 20-26. IEEE, 2014.

Beasley, J E. “Operations Research (OR)-Library.†Index of /~Mastjjb/Jeb/Orlib/Files, 2004, http://people.brunel.ac.uk/~mastjjb/jeb/orlib/files

Nadia Abd-Alsabour. (2019). Table2 The correlation between all pairs of the 31 items. http://doi.org/10.5281/zenodo.2649226

R: A Language and Environment for Statistical Computing [http://www.R-project.org]. R Foundation for Statistical Computing, Vienna, Austria.

S. Das, A. Abraham, and A. Konar, "Particle Swarm Optimization and Differential Evolution Algorithms: Technical Analysis, Applications and Hybridization Perspectives", Studies in Computational Intelligence, vol.116, pp.1–38, Springer-Verlag Berlin Heidelberg, 2008.

N. Abd-Alsabour, "Investigating the influence of adding local search to search algorithms", 2017 18th International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT), 2017.

N. Abd-Alsabour, "Parallel evolutionary algorithms and high dimensional optimization problems", Journal of Computers, vol. 13, no. 11, pp.1265-1271, November 2018.

N. Abd-Alsabour, "Local search for parallel optimization algorithms for high dimensional optimization problems", MATEC Web of Conferences, vol. 210, p. 04052. EDP Sciences, 2018.

J. Zhang, and A. C. Sanderson, "JADE: Adaptive Differential Evolution with Optional External Archive", IEEE Transactions on Evolutionary Computation, vol.13, no. 5, pp. 945-958, 2009.

DOI: http://dx.doi.org/10.18517/ijaseit.9.2.7824


  • There are currently no refbacks.

Published by INSIGHT - Indonesian Society for Knowledge and Human Development