Version 1.0.4 (9.76 KB) by Quoc Bao Diep
SOMA codes are no longer updated on this MathWorks website. Please visit the author's GitHub for updates: https://github.com/diepquocbao
Updated 7 Jan 2022
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.
Diep, Quoc Bao. "Self-Organizing Migrating Algorithm Team To Team Adaptive–SOMA T3A." In 2019 IEEE Congress on Evolutionary Computation (CEC), pp. 1182-1187. IEEE, 2019. doi:10.1109/cec.2019.8790202.
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