caa a22 4500 605468672 CHVBK 20210128100318.0 cr unu---uuuuu 210128e20150601xx s 000 0 eng 10.1007/s00500-014-1283-z doi (NATIONALLICENCE)springer-10.1007/s00500-014-1283-z A two-layer surrogate-assisted particle swarm optimization algorithm [Elektronische Daten] [Chaoli Sun, Yaochu Jin, Jianchao Zeng, Yang Yu] Like most evolutionary algorithms, particle swarm optimization (PSO) usually requires a large number of fitness evaluations to obtain a sufficiently good solution. This poses an obstacle for applying PSO to computationally expensive problems. This paper proposes a two-layer surrogate-assisted PSO (TLSAPSO) algorithm, in which a global and a number of local surrogate models are employed for fitness approximation. The global surrogate model aims to smooth out the local optima of the original multimodal fitness function and guide the swarm to fly quickly to an optimum or the global optimum. In the meantime, a local surrogate model constructed using the data samples near each particle is built to achieve a fitness estimation as accurate as possible. The contribution of each surrogate in the search is empirically verified by experiments on uni- and multi-modal problems. The performance of the proposed TLSAPSO algorithm is examined on ten widely used benchmark problems, and the experimental results show that the proposed algorithm is effective and highly competitive with the state-of-the-art, especially for multimodal optimization problems. Springer-Verlag Berlin Heidelberg, 2014 Particle swarm optimization nationallicence Surrogate-assisted optimization nationallicence Computationally expensive optimization problems nationallicence Sun Chaoli Complex System and Computational Intelligence Laboratory, Taiyuan University of Science and Technology, 030024, Taiyuan, Shanxi, China aut Jin Yaochu Department of Computing, University of Surrey, GU2 7XH, Guildford, UK aut Zeng Jianchao Complex System and Computational Intelligence Laboratory, Taiyuan University of Science and Technology, 030024, Taiyuan, Shanxi, China aut Yu Yang National Key Laboratory for Novel Software Technology, Nanjing University, 200093, Nanjing, China aut Soft Computing Springer Berlin Heidelberg 19/6(2015-06-01), 1461-1475 1432-7643 19:6<1461 2015 19 500 https://doi.org/10.1007/s00500-014-1283-z text/html Onlinezugriff via DOI BK010053 XK010053 XK010000 Metadata rights reserved Springer special CC-BY-NC licence nationallicence 1 research-article jats NATIONALLICENCE NATIONALLICENCE NL-springer NATIONALLICENCE 856 40 https://doi.org/10.1007/s00500-014-1283-z text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Sun Chaoli Complex System and Computational Intelligence Laboratory, Taiyuan University of Science and Technology, 030024, Taiyuan, Shanxi, China aut NATIONALLICENCE 700 1- Jin Yaochu Department of Computing, University of Surrey, GU2 7XH, Guildford, UK aut NATIONALLICENCE 700 1- Zeng Jianchao Complex System and Computational Intelligence Laboratory, Taiyuan University of Science and Technology, 030024, Taiyuan, Shanxi, China aut NATIONALLICENCE 700 1- Yu Yang National Key Laboratory for Novel Software Technology, Nanjing University, 200093, Nanjing, China aut NATIONALLICENCE 773 0- Soft Computing Springer Berlin Heidelberg 19/6(2015-06-01), 1461-1475 1432-7643 19:6<1461 2015 19 500