A Comparative Study Between Artificial Bee Colony (ABC) Algorithm and Its Variants on Big Data Optimization

Abstract

The big data term and its formal definition have changed the properties of some of the computational problems. One of the problems for which the fundamental properties change with the existence of the big data is the optimization problems. Artificial bee colony (ABC) algorithm inspired by the intelligent source search, consumption and communication characteristics of the real honey bees has proven its efficiency on solving different numerical and combinatorial optimization problems. In this study, the standard ABC algorithm and its well-known variants including the gbest-guided ABC algorithm, the differential evolution based ABC/best/1 and ABC/best/2 algorithms, crossover ABC algorithm, converge-onlookers ABC algorithm and quick ABC algorithm were assessed using the electroencephalographic signal decomposition based optimization problems introduced at the 2015 Congress on Evolutionary Computing Big Data Competition. The experimental studies on solving big data optimization problems showed that the phase-divided structure of the standard ABC algorithm still protects its advantageous sides when the candidate food sources or solutions are generated by referencing the global best solution in the onlooker bee phase. © 2020, Springer-Verlag GmbH Germany, part of Springer Nature.