Abstract
This paper presents a novel parallel framework based on the Multi-Population Cultural Algorithm (MPCA) scheme for optimization problems. Contrary to the existing variants of Cultural Algorithm (CA), the proposed parallel framework for MPCA (PFMPCA) allows the use of any implemented metaheuristic both in a belief, and in a population space. Furthermore, the proposed approach permits CA to evolve simultaneously multiple population and belief sub-spaces, leveraging the dual inheritance mechanism and utilizing multi-population approach. Moreover, each sub-population (in population or belief space) is able to communicate between each other. PFMPCA has been implemented on Graphics Processing Units (GPUs) using CUDA programming model. The performance of the developed framework was evaluated using asymmetric Travelling Salesman Problem (ATSP). The MPCA for TSP implemented by means of the parallel framework proves to have an extensible architecture designed to accommodate changes and good performances.
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The work was supported by statutory grant of the Wroclaw University of Science and Technology, Poland.
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OU initiated and designed the study, supervised the work, made statistical tests. RT implemented the framework, performed the experiments. Both authors wrote and approved the final manuscript.
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Unold, O., Tarnawski, R. (2018). A Parallel Framework for Multi-Population Cultural Algorithm and Its Applications in TSP. In: Nicosia, G., Pardalos, P., Giuffrida, G., Umeton, R. (eds) Machine Learning, Optimization, and Big Data. MOD 2017. Lecture Notes in Computer Science(), vol 10710. Springer, Cham. https://doi.org/10.1007/978-3-319-72926-8_39
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