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14-11-2019 | Original Article | Issue 3/2020

International Journal of Machine Learning and Cybernetics 3/2020

Large-scale evolutionary optimization: a survey and experimental comparative study

Journal:
International Journal of Machine Learning and Cybernetics > Issue 3/2020
Authors:
Jun-Rong Jian, Zhi-Hui Zhan, Jun Zhang
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Abstract

In the last decades, global optimization problems are very common in many research fields of science and engineering and lots of evolutionary computation algorithms have been used to deal with such problems, such as differential evolution (DE) and particle swarm optimization (PSO). However, the algorithms performance rapidly decreases as the increasement of the problem dimension. In order to solve large-scale global optimization problems more efficiently, a lot of improved evolutionary computation algorithms, especially the improved DE or improved PSO algorithms have been proposed. In this paper, we want to analyze the differences and characteristics of various large-scale evolutionary optimization (LSEO) algorithms on some benchmark functions. We adopt the CEC2010 and the CEC2013 large-scale optimization benchmark functions to compare the performance of seven well-known LSEO algorithms. Then, we try to figure out which algorithms perform better on different types of benchmark functions based on simulation results. Finally, we give some potential future research directions of LSEO algorithms and make a conclusion.

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