Abstract
In this work, a novel hybrid population-based algorithm, named SP-QPSO has been introduced by combining Shuffled Complex Evolution with PCS (SP-UCI) and Quantum Particle Swarm Optimization (QPSO). The main purpose of this algorithm is to improve the efficiency of optimization task in both low and high dimensional problems. SP-QPSO is using the main strategy of SP-UCI by constructing complexes and monitoring their dimensionality, then evolving each complex based on QPSO. In this algorithm the initialization of point is done using Centroidal Voronoi Tessellations (CVT) to ensure that points visit the entire search space. Twelve popular benchmark functions are employed to evaluate the SP-QPSO performance in 2, 10, 50, 100, and 200 Dimensions. The results show that the proposed algorithm performed better in most functions.
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Acknowledgments
This study was funded by University of Malaya Research Grant (UMRG) project RG115-12ICT project title of Creative Learning for Emotional Expression of Robot Partners Using Interactive Particle Swarm Optimization.
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Taherzadeh, G., Loo, C.K., Chaw, L.T. (2015). A Novel Hybrid SP-QPSO Algorithm Using CVT for High Dimensional Problems. In: Gao, D., Ruan, N., Xing, W. (eds) Advances in Global Optimization. Springer Proceedings in Mathematics & Statistics, vol 95. Springer, Cham. https://doi.org/10.1007/978-3-319-08377-3_34
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DOI: https://doi.org/10.1007/978-3-319-08377-3_34
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