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ARAE-SOM plus BCO: An enhanced artificial raindrop algorithm using self-organizing map and binomial crossover operator
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DOI：10.1016/j.neucom.2017.11.045.png)
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Abstract
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Artificial raindrop algorithm (ARA) has been shown to be an effective methodology for solving optimization problems over continuous space. To further enhance the search performance of ARA, self-organizing map (SOM) and binomial crossover operator (BCO) are integrated into ARA. The purpose of the former is to enhance the diversity of population by dynamically dividing the current population into several sub-populations. The latter is to make the function landscape be pseudo-separable by covariance matrix learning, which efficiently directs the evolution of the population toward a global optimum. By incorporating the SOM and BCO into ARA, an enhanced ARA variant, which is called ARAE-SOM+BCO for short, is presented in this paper. For the comparison purposes, the forehand fourteen CEC2005 contest benchmark functions are selected as the test suite to evaluate the performance of the proposed ARAE-SOM+BCO. The experiment results show that the performance of ARAE-SOM+BCO significantly outperforms ARA and its extension variant, and is also competitive with other state-of-the-art evolutionary algorithms in most test functions. (C) 2017 Elsevier B.V. All rights reserved.
Keywords:
Artificial raindrop algorithm
Self-organizing map
Binomial crossover operator
Covariance matrix learning
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