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Selected Publications

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New papers:

TOP and Strong improvements of SOMA performance!!!

Quoc Bao Diep, Ivan Zelinka, and Swagatam Das. 2019. Self-Organizing Migrating Algorithm for the 100-Digit Challenge. In Proceedings of the Genetic and Evolutionary Computation Conference 2019 (GECCO ’19). ACM, New York, NY, USA

In this paper, we apply the SOMA T3A algorithm to solve 10 hard problems of the 100-Digit Challenge of the GECCO 2019 Competition. With effective improvements in choosing Migrants and Leader in the organization process, as well as the Step and PRT adaptive parameters in the migration process, the algorithm has achieved
promising results. The total score that the algorithm achieved is 92.04 points.

The source code of the SOMA T3A algorithm is publicly available at

TOP and Strong improvements of SOMA performance!!!

Quoc Bao Diep, 2019. Self-Organizing Migrating Algorithm Team To Team Adaptive – SOMA T3A. In Proceedings of the CEC 2019, Wellington, New Zealand


Abstract—Swarm intelligence algorithm and its variants are constantly evolving over the years, the SOMA algorithm is also not out of that trend. In this paper, we propose a novel strategy of SOMA, called SOMA T3A. The proposed algorithm is divided into three main processes, namely Organization, Migration, and Update. Migrants are selected from the initial population and migrate towards the selected Leader according to the organization process. The Step and PRT parameters are no longer fixed like in the original version; instead, they are adapted through each migration loop. The performance of the algorithm is proven on the 58 well-known benchmark problems from the CEC2013 as well as CEC2017 benchmark suites. The results are compared with the SOMA family and compared with the state-of-the-art algorithms to show its promising performance. 

The source code of the SOMA T3A algorithm is publicly available at

TOP and Strong improvements of SOMA performance!!!

Quoc Bao Diep, Ivan Zelinka, and Swagatam Das. 2019. PARETO-BASED SELF-ORGANIZING MIGRATING ALGORITHM. In the Mendel journal 2019 Brno, Czech Republic

In this paper, we propose a new method named Pareto-based self-organizing migrating algorithm
(SOMA Pareto), in which the algorithm is divided into the Organization, Migration, and Update processes. The
important key in the Organization process is the application of the Pareto Principle to select the Migrant and the
Leader, increasing the performance of the algorithm. The adaptive PRT, Step, and PRTVector parameters are
applied to enhance the ability to search for promising subspaces and then to focus on exploiting that subspaces.
Based on the testing results on the well-known benchmark suites such as CEC’13, CEC’15, and CEC’17, the
superior performance of the proposed algorithm compared to the SOMA family and the state-of-the-art algorithms
such as variant DE and PSO are confirmed. These results demonstrate that SOMA Pareto is an effective,
promising algorithm.

The source code of the PSOMA algorithm is publicly available at

Older papers:

Zelinka, Ivan. "SOMA—self-organizing migrating algorithm." In New optimization techniques in engineering, pp. 167-217. Springer, Berlin, Heidelberg, 2004.


Davendra, Donald, and Ivan Zelinka. "Self-organizing migrating algorithm." New Optimization Techniques in Engineering (2016).


Zelinka, Ivan, and Lubomir Sikora. "StarCraft: Brood War—Strategy powered by the SOMA swarm algorithm." In Computational Intelligence and Games (CIG), 2015 IEEE Conference on, pp. 511-516. IEEE, 2015.


Zelinka, Ivan, Martin Němec, and Roman Šenkeřík. "Gamesourcing: Perspectives and Implementations." In Simulation and Gaming. InTech, 2018. Downloadable here.


Singh, Dipti, and Seema Agrawal. "Self organizing migrating algorithm with quadratic interpolation for solving large scale global optimization problems." Applied Soft Computing 38 (2016): 1040-1048.


Singh, Dipti, and Seema Agrawal. "Hybridization of self organizing migrating algorithm with mutation for global optimization." In Proceedings of the international conference on mathematical sciences (ICMS), Elsevier, pp. 605-609. 2014.


Singh, Dipti, and Seema Agrawal. "A novel hybrid self organizing migrating algorithm with mutation for global optimization." International Journal of Soft Computing and Engineering 3, no. 6 (2014): 101-106.


Deep, Kusum. "A self-organizing migrating genetic algorithm for constrained optimization." Applied Mathematics and Computation 198, no. 1 (2008): 237-250.


Davendra, Donald, Ivan Zelinka, Magdalena Bialic-Davendra, Roman Senkerik, and Roman Jasek. "Discrete self-organising migrating algorithm for flow-shop scheduling with no-wait makespan." Mathematical and Computer Modelling 57, no. 1-2 (2013): 100-110.


dos Santos Coelho, Leandro, and Piergiorgio Alotto. "Electromagnetic optimization using a cultural self-organizing migrating algorithm approach based on normative knowledge." IEEE Transactions on Magnetics 45, no. 3 (2009): 1446-1449.


Onwubolu, Godfrey C., and B. V. Babu. New optimization techniques in engineering. Vol. 141. Springer, 2013.


dos Santos Coelho, Leandro, and Viviana Cocco Mariani. "An efficient cultural self-organizing migrating strategy for economic dispatch optimization with valve-point effect." Energy Conversion and Management 51, no. 12 (2010): 2580-2587.


dos Santos Coelho, Leandro. "Self-organizing migrating strategies applied to reliability-redundancy optimization of systems." IEEE Transactions on Reliability 58, no. 3 (2009): 501-510.


Nolle, Lars, Ivan Zelinka, Adrian A. Hopgood, and Alec Goodyear. "Comparison of a self-organizing migration algorithm with simulated annealing and differential evolution for automated waveform tuning." Advances in Engineering Software 36, no. 10 (2005): 645-653.


Deep, Kusum. "A new hybrid self-organizing migrating genetic algorithm for function optimization." In Evolutionary Computation, 2007. CEC 2007. IEEE Congress on, pp. 2796-2803. IEEE, 2007.


Pospisilik, Martin, Lukas Kouril, Ivo Motyl, and Milan Adámek. "Single and double layer spiral planar inductors optimisation with the aid of a self-organising migrating algorithm." In Proceedings of the 11th WSEAS International Conference on Signal Processing, Computational Geometry and Artificial Vision. Venice: WSEAS Press (IT), pp. 272-277. 2011.


Davendra, Donald, Ivan Zelinka, Roman Senkerik, and Michal Pluhacek. "Complex network analysis of the discrete self-organising migrating algorithm." In Nostradamus 2014: Prediction, Modeling and Analysis of Complex Systems, pp. 161-174. Springer, Cham, 2014.


Hadaš, Z., Č. Ondrůšek, and J. Kurfürst. "Optimization of vibration power generator parameters using self-organizing migrating algorithm." In Recent Advances in Mechatronics, pp. 245-250. Springer, Berlin, Heidelberg, 2010.


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