Self-Organizing Migrating Algorithm Team To Team Adaptive - SOMA T3A

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, an...

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Bibliographic Details
Published in:	2019 IEEE Congress on Evolutionary Computation (CEC) pp. 1182 - 1187
Main Author:	Diep, Quoc Bao
Format:	Conference Proceeding
Language:	English
Published:	IEEE 01.06.2019
Subjects:	Approximation algorithms Benchmark testing Optimization Organizations Particle swarm optimization Self-organizing migrating algorithm single objective optimization Sociology Statistics swarm intelligence
Online Access:	Get full text
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Summary:	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.
DOI:	10.1109/CEC.2019.8790202