Review of Software Tools for Working with Evolutionary and Swarm Optimization Methods
Abstract
The article is devoted to a review of software tools that allow applying, developing and investigating evolutionary and swarm optimization methods for solving complex discrete and continuous optimization problems. The article considers various types and kinds of optimization problems arising in applied problems, including multi-objective optimization problems. The concept of a population optimization algorithm is formalized and the main classes of algorithms of this type are considered, including evolutionary algorithms, swarm algorithms and multiparticle algorithms. The results of a detailed analysis of thirteen modern most popular frameworks for working with evolutionary and swarm optimization algorithms are presented. The main goal of the analysis is to study the capabilities provided by these software tools for creating, customizing and using population optimization algorithms for solving applied optimization problems. In particular, the types of optimization problems supported by the considered software tools, as well as the presence of built-in testing tools and sets of test optimization problems are analyzed. A special section of the study is devoted to the analysis of support of parallel computing by frameworks under consideration, since it is known that the use of population algorithms, on the one hand, is computationally expensive, and on the other hand, such algorithms have significant potential for parallelization. Based on the results of the review, recommendations are given on the use of the considered software tools in various scenarios of their practical use.
References
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3. Passino K.M. Biomimicry of bacterial foraging for distributed optimization and control. IEEE control systems magazine. 2002;22(3):52-67. https://doi.org/10.1109/MCS.2002.1004010
4. Corana A., et al. Minimizing multimodal functions of continuous variables with the "simulated annealing" algorithm. ACM Transactions on Mathematical Software. 1987;13(3):262-280. https://doi.org/10.1145/29380.29864
5. Chen W.N., Tan D.Z. Set-based discrete particle swarm optimization and its applications: a survey. Frontiers of Computer Science. 2018;12:203-216. https://doi.org/10.1007/s11704-018-7155-4
6. Khuri S., Bäck T., Heitkötter J. An Evolutionary Approach to Combinatorial Optimization Problems. ACM Conference on Computer Science. 1994:66-73. https://doi.org/10.1145/197530.197558
7. Bianchi L., et al. A survey on metaheuristics for stochastic combinatorial optimization. Natural Computing. 2009;8:239-287. https://doi.org/10.1007/s11047-008-9098-4
8. Janecek A., Tan Y. Using Population Based Algorithms for Initializing Nonnegative Matrix Factorization. In: Tan Y., Shi Y., Chai Y., Wang G. (eds.) Advances in Swarm Intelligence. ICSI 2011. Lecture Notes in Computer Science. Vol. 6729. Berlin, Heidelberg: 2011. p. 307-316. https://doi.org/10.1007/978-3-642-21524-7_37
9. Chen X.Y., Chau K.W., Busari A.O. A comparative study of population-based optimization algorithms for downstream river flow forecasting by a hybrid neural network model. Engineering Applications of Artificial Intelligence. 2015;46:258-268. https://doi.org/10.1016/j.engappai.2015.09.010
10. Arnold D.V., Beyer H.G. Noisy Optimization with Evolution Strategies. Universität Dortmund; 2002. 20 p. http://dx.doi.org/10.17877/DE290R-14966
11. Patle B.K., et al. Path planning in uncertain environment by using firefly algorithm. Defence technology. 2018;14(6):691-701. https://doi.org/10.1016/j.dt.2018.06.004
12. Askarzadeh A. A novel metaheuristic method for solving constrained engineering optimization problems: crow search algorithm. Computers & structures. 2016;169:1-12. https://doi.org/10.1016/j.compstruc.2016.03.001
13. Kaboli S.H.A., Selvaraj J., Rahim N.A. Rain-fall optimization algorithm: A population based algorithm for solving constrained optimization problems. Journal of Computational Science. 2017;19:31-42. https://doi.org/10.1016/j.jocs.2016.12.010
14. Deb K. Multi-objective optimisation using evolutionary algorithms: an introduction // Multi-objective evolutionary optimisation for product design and manufacturing. London: Springer London, 2011. https://doi.org/10.1007/978-0-85729-652-8_1
15. Rajpurohit J., et al. Glossary of metaheuristic algorithms. International Journal of Computer Information Systems and Industrial Management Applications. 2017;9:25-25. Available at: https://cspub-ijcisim.org/index.php/ijcisim/article/view/353 (accessed 17.01.2025).
16. Cantu-Paz E., Goldberg D.E. On the scalability of parallel genetic algorithms. Evolutionary computation. 1999;7(4):429-449. https://doi.org/10.1162/evco.1999.7.4.429
17. Essaid M., et al. GPU parallelization strategies for metaheuristics: a survey. International Journal of Parallel, Emergent and Distributed Systems. 2019;34(5):497-522. https://doi.org/10.1080/17445760.2018.1428969
18. Parejo J.A., Ruiz-Cortés A., Lozano S., Fernandez P. Metaheuristic optimization frameworks: a survey and benchmarking. Soft Computing. 2012;16(3):527-561. https://doi.org/10.1007/s00500-011-0754-8
19. Ramírez A., Barbudo R., Romero J.R. An experimental comparison of metaheuristic frameworks for multi‐objective optimization. Expert Systems. 2023;40(4):e12672. https://doi.org/10.1111/exsy.12672
20. Talbi E. G. Automated design of deep neural networks: A survey and unified taxonomy. ACM Computing Surveys. 2021;54(2):1-37. https://doi.org/10.1145/3439730
21. Huang H.Z., Zhang X. Design optimization with discrete and continuous variables of aleatory and epistemic uncertainties. Journal of Mechanical Design. 2009;131(3):031006. https://doi.org/10.1115/1.3066712
22. Kokkolaras M., Audet C., Dennis J. E. Mixed variable optimization of the number and composition of heat intercepts in a thermal insulation system. Optimization and Engineering. 2001;2:5-29. https://doi.org/10.1023/A:1011860702585
23. Gutjahr W.J., Strauss C., Wagner E. A stochastic branch-and-bound approach to activity crashing in project management. INFORMS journal on computing. 2000;12(2):125-135. https://doi.org/10.1287/ijoc.12.2.125.11894
24. Patle B.K., et al. Path planning in uncertain environment by using firefly algorithm. Defence technology. 2018;14(6):691-701. https://doi.org/10.1016/j.dt.2018.06.004
25. Mahanta B.K., Chakraborti N. Tri-objective optimization of noisy dataset in blast furnace iron-making process using evolutionary algorithms. Materials and Manufacturing Processes. 2020;35(6):677-686. https://doi.org/10.1080/10426914.2019.1643472
26. Emmerich M.T.M., Deutz A.H. A tutorial on multiobjective optimization: fundamentals and evolutionary methods. Natural computing. 2018.17:585-609. https://doi.org/10.1007/s11047-018-9685-y
27. De Castro L.N., Von Zuben F.J. Learning and optimization using the clonal selection principle. IEEE transactions on evolutionary computation. 2002;6(3):239-251. https://doi.org/10.1109/TEVC.2002.1011539
28. Reynolds C.W. Flocks, herds and schools: A distributed behavioral model. In: Proceedings of the 14th annual conference on Computer graphics and interactive techniques. (SIGGRAPH '87). New York, NY, USA: Association for Computing Machinery; 1987. p. 25-34. https://doi.org/10.1145/37401.37406
29. Dorigo M., Birattari M., Stutzle T. Ant colony optimization. IEEE computational intelligence magazine. 2006;1(4):28-39. https://doi.org/10.1109/MCI.2006.329691
30. Pham D.T., et al. The Bees Algorithm A Novel Tool for Complex Optimisation Problems. Intelligent production machines and systems. 2nd I*PROMS Virtual International Conference 3-14 July 2006. Elsevier Science Ltd; 2006. p. 454-459. https://doi.org/10.1016/B978-008045157-2/50081-X
31. Krishnanand K.N., Ghose D. Glowworm swarm optimisation: a new method for optimising multi-modal functions. International Journal of Computational Intelligence Studies. 2009;1(1):93-119. https://doi.org/10.1504/IJCISTUDIES.2009.025340
32. Cheng R., Jin Y. A competitive swarm optimizer for large scale optimization. IEEE transactions on cybernetics. 2014;45(2):191-204. https://doi.org/10.1109/TCYB.2014.2322602
33. Rashedi E., Nezamabadi-Pour H., Saryazdi S. GSA: a gravitational search algorithm. Information sciences. 2009;179(13):2232-2248. https://doi.org/10.1016/j.ins.2009.03.004
34. Rabanal P., Rodríguez I., Rubio F. Using River Formation Dynamics to Design Heuristic Algorithms. In: Akl S.G., Calude C.S., Dinneen M.J., Rozenberg G., Wareham H.T. (eds.) Unconventional Computation. UC 2007. Lecture Notes in Computer Science. Vol. 4618. Berlin, Heidelberg: Springer; 2007. p. 163-177. https://doi.org/10.1007/978-3-540-73554-0_16
35. Miranda L.J. PySwarms: a research toolkit for Particle Swarm Optimization in Python. Journal of Open Source Software. 2018;3(21):433. https://doi.org/10.21105/joss.00433
36. Biscani F., Izzo D. A parallel global multiobjective framework for optimization: pagmo. Journal of Open Source Software. 2020;5(53):2338. https://doi.org/10.21105/joss.02338
37. Fortin F.A., et al. DEAP: Evolutionary algorithms made easy. The Journal of Machine Learning Research. 2012;13(1):2171-2175. Available at: https://dl.acm.org/doi/10.5555/2503308.2503311 (accessed 17.01.2025).
38. Blank J., Deb K. Pymoo: Multi-objective optimization in python. IEEE Access. 2020;8:89497-89509. https://doi.org/10.1109/ACCESS.2020.2990567
39. Durillo J.J., Nebro A.J. jMetal: A Java framework for multi-objective optimization. Advances in engineering software. 2011;42(10):760-771. https://doi.org/10.1016/j.advengsoft.2011.05.014
40. Coletti M.A., Scott E.O., Bassett J.K. Library for evolutionary algorithms in Python (LEAP). In: Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion (GECCO '20). New York, NY, USA: Association for Computing Machinery; 2020. p. 1571-1579. https://doi.org/10.1145/3377929.3398147
41. Dreo J., et al. Paradiseo: from a modular framework for evolutionary computation to the automated design of metaheuristics: 22 years of Paradiseo. In: Proceedings of the Genetic and Evolutionary Computation Conference Companion (GECCO '21). New York, NY, USA: Association for Computing Machinery; 2021. p. 1522-1530. https://doi.org/10.1145/3449726.3463276
42. Collet P., Lutton E., Schoenauer M., Louchet J. Take It EASEA. In: Schoenauer, M., et al. Parallel Problem Solving from Nature PPSN VI. PPSN 2000. Lecture Notes in Computer Science. Vol. 1917. Berlin, Heidelberg: Springer; 2000. p. 891-901. https://doi.org/10.1007/3-540-45356-3_87
43. Wagner S., et al. Architecture and Design of the HeuristicLab Optimization Environment. In: Klempous R., Nikodem J., Jacak W., Chaczko Z. (eds.) Advanced Methods and Applications in Computational Intelligence. Topics in Intelligent Engineering and Informatics. Vol. 6. Heidelberg: Springer; 2014. p. 197-261. https://doi.org/10.1007/978-3-319-01436-4_10
44. Lukasiewycz M., Glaß M., Reimann F., Teich J. Opt4J: a modular framework for meta-heuristic optimization. In: Proceedings of the 13th annual conference on Genetic and evolutionary computation (GECCO '11). New York, NY, USA: Association for Computing Machinery; 2011. p. 1723-1730. https://doi.org/10.1145/2001576.2001808
45. Navarro R., Nápoles G. Classical niching methods for multimodal optimization: a brief review. Revista Cubana de Ciencias Informáticas. 2013;7(2):137-153. Available at: https://rcci.uci.cu/index.php/RCCI/article/view/430 (accessed 17.01.2025). (In Spain., abstract in Eng.)
46. Jiang S., Kaiser M., Wan S., Guo J., Yang S., Krasnogor N. An Empirical Study of Dynamic Triobjective Optimisation Problems. In: 2018 IEEE Congress on Evolutionary Computation (CEC). Rio de Janeiro, Brazil: IEEE Press; 2018. p. 1-8. https://doi.org/10.1109/CEC.2018.8477667
47. Zitzler E., Deb K., Thiele L. Comparison of multiobjective evolutionary algorithms: Empirical results. Evolutionary computation. 2000;8(2):173-195. https://doi.org/10.1162/106365600568202
48. Deb K., Thiele L., Laumanns M., Zitzler E. Scalable Test Problems for Evolutionary Multiobjective Optimization. In: Abraham A., Jain L., Goldberg R. (eds.) Evolutionary Multiobjective Optimization. Advanced Information and Knowledge Processing. London: Springer; 2005. p. 105-145. https://doi.org/10.1007/1-84628-137-7_6
49. Virtanen P., et al. SciPy 1.0: fundamental algorithms for scientific computing in Python. Nature methods. 2020;17(3):261-272. https://doi.org/10.1038/s41592-019-0686-2
50. de Nobel J., Ye F., Vermetten D., Wang H., Doerr C., Bäck T. IOHexperimenter: Benchmarking Platform for Iterative Optimization Heuristics. Evolutionary Computation. 2024;32(3):205-210. https://doi.org/10.1162/evco_a_00342
51. Telikani A., et al. Evolutionary machine learning: A survey. ACM Computing Surveys. 2021;54(8):1-35. https://doi.org/10.1145/3467477
52. Zhang Y., et al. Evolutionary Computation in bioinformatics: A survey. Neurocomputing. 2024;591:127758. https://doi.org/10.1016/j.neucom.2024.127758
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Published
2025-04-28
How to Cite
NIKOLASHKIN, Aleksei Gerasimovich; ERSHOV, Nikolay Mikhailovich.
Review of Software Tools for Working with Evolutionary and Swarm Optimization Methods.
Modern Information Technologies and IT-Education, [S.l.], v. 21, n. 1, p. 113-126, apr. 2025.
ISSN 2411-1473. Available at: <http://sitito.cs.msu.ru/index.php/SITITO/article/view/1120>. Date accessed: 15 july 2026.
doi: https://doi.org/10.25559/SITITO.021.202501.113-126.
Section
Scientific software in education and science
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