Abstract
We present a new Java package, named binMeta, for the development and the study of meta-heuristic searches for global optimization. The solution space for our optimization problems is based on a discrete representation, but it does not restrict to combinatorial problems, for every representation on computer machines finally reduces to a sequence of bits. We focus on general purpose meta-heuristics, which are not tailored to any specific subclass of problems. Although we are aware that this is not the first attempt to develop one unique tool implementing more than one meta-heuristic search, we are motivated by the following three main research lines on meta-heuristics. First, we plan to collect several implementations of meta-heuristic searches, developed by several programmers under the common interface of the package, where a particular attention is given to the common components of the various meta-heuristics. Second, the discrete representation for the solutions that we employ allows the user to perform a preliminary study on the degrees of freedom that is likely to give a positive impact on the performance of the meta-heuristic searches. Third, the choice of Java as a programming language is motivated by its flexibility and the use of a high-level objective-oriented paradigm. Finally, an important point in the development of binMeta is that a meta-heuristic search implemented in the package can also be seen as an optimization problem, where its parameters play the role of decision variables.
This is a preview of subscription content, access via your institution.
Buying options
References
Atanassova, V., Fidanova, S., Popchev, I., Chountas, P.: Generalized nets, ACO-algorithms and genetic algorithm. In: Sabelfeld, K.K., Dimov, I. (eds.) Monte Carlo Methods and Applications, pp. 39–46. De Gruyter (2012)
Cahon, S., Melab, N., Talbi, E.-G.: ParadisEO: a framework for the reusable design of parallel and distributed metaheuristics. J. Heuristics 10, 357–380 (2004)
Crawford, B., Soto, R., Astorga, G., García, J., Castro, C., Paredes, F.: Putting continuous metaheuristics to work in binary search spaces. Complexity 2017 (2017). Article ID 8404231, 19 p.
Dorigo, M., Birattari, M.: Ant colony optimization. In: Sammut, C., Webb, G.I. (eds.) Encyclopedia of Machine Learning, pp. 36–39. Springer, Boston (2010). https://doi.org/10.1007/978-0-387-30164-8
Durillo, J.J., Nebro, A.J.: jMetal: a Java framework for multi-objective optimization. Adv. Eng. Softw. 42, 760–771 (2011)
Fister Jr, I., Yang, X.-S., Fister, I., Brest, J., Fister, D.: A brief review of nature-inspired algorithms for optimization. Elektrotehniski Vestnik 80(3), 1–7 (2013)
Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Adv. Eng. Softw. 69, 46–61 (2014)
Mucherino, A.: An analysis on the degrees of freedom of binary representations for solutions to discretizable distance geometry problems. In: Fidanova, S. (eds) WCO 2020. SCI, vol. 986, pp. 251–255. Springer, Cham (2022). https://doi.org/10.1007/978-3-030-82397-9_13
Mucherino, A., Fidanova, S., Ganzha, M.: Ant colony optimization with environment changes: an application to GPS surveying. In: IEEE Conference Proceedings, Federated Conference on Computer Science and Information Systems (FedCSIS15), Workshop on Computational Optimization (WCO15), Lodz, Poland, pp. 495–500 (2015)
Mucherino, A., Seref, O.: Modeling and solving real-life global optimization problems with meta-heuristic methods. In: Pardalos, P.M., Papajorgji , P.J. (eds.) Advances in Modeling Agricultural Systems. SOIA, vol. 25, pp. 403–419. Springer, Boston (2009). https://doi.org/10.1007/978-0-387-75181-8_19
Pressman, R.S., Maxim, B.R.: Software Engineering: A Practitioner’s Approach, 9th edn. McGraw-Hill Education (2019). 704 p.
Pukhkaiev, D., Semendiak, Y., Götz, S., Aßmann, U.: Combined selection and parameter control of meta-heuristics. In: IEEE Conference Proceedings, Symposium Series on Computational Intelligence (SSCI20), Canberra, Australia, pp. 3125–3132 (2020)
Sivanandam, S., Deepa, S.: Introduction to Genetic Algorithms. Springer, Heidelberg (2008). 442 p. https://doi.org/10.1007/978-3-540-73190-0
Sörensen, K., Glover, F.: Metaheuristics, encyclopedia of operations research and management. Science 62, 960–970 (2013)
Tamura, K., Yasuda, K.: Spiral optimization algorithm using periodic descent directions. SICE J. Control Meas. Syst. Integr. 9(3), 134–143 (2016)
Tang, R., Fong, S., Yang, X.S., Deb, S.: Wolf search algorithm with ephemeral memory. In: IEEE Proceedings, 7th International Conference on Digital Information Management (ICDIM 2012), Macau, pp. 165–172 (2012)
Wiles, A.: Modular elliptic curves and Fermat’s last theorem. Ann. Math. 141(3), 443–551 (1995)
Woeginger, G.J.: Exact algorithms for NP-hard problems: a survey. In: Jünger, M., Reinelt, G., Rinaldi, G. (eds.) Combinatorial Optimization — Eureka, You Shrink! LNCS, vol. 2570, pp. 185–207. Springer, Heidelberg (2003). https://doi.org/10.1007/3-540-36478-1_17
Acknowledgments
Throughout the entire article, the reader may have noticed that the plural form is employed even if there is only one author. This author actually needs to thank the collaboration of some Master students that worked on this software package in the framework of course projects. The identity of the students that gave the most important contributions appear (in different forms) in the source files (see GitHub repository).
This work is partially supported by the international project multiBioStruct funded by the ANR French funding agency (ANR-19-CE45-0019).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 Springer Nature Switzerland AG
About this paper
Cite this paper
Mucherino, A. (2022). binMeta: A New Java Package for Meta-heuristic Searches. In: Lirkov, I., Margenov, S. (eds) Large-Scale Scientific Computing. LSSC 2021. Lecture Notes in Computer Science, vol 13127. Springer, Cham. https://doi.org/10.1007/978-3-030-97549-4_28
Download citation
DOI: https://doi.org/10.1007/978-3-030-97549-4_28
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-97548-7
Online ISBN: 978-3-030-97549-4
eBook Packages: Computer ScienceComputer Science (R0)