Article: Adaptive surrogate-based swarm intelligence algorithm and its application in wastewater treatment processes Journal: International Journal of Bio-Inspired Computation (IJBIC) 2023 Vol.21 No.2 pp.81 - 93 Abstract: To solve the computationally expensive optimisation problems effectively, a surrogate-based swarm intelligence algorithm is presented. A global surrogate model and a local one are respectively built to approximately evaluate the fitness values of the individuals instead of the accurate function during the evolution process. Following that, the control strategy of swarm evolution and the surrogate management are designed in detail to improve the efficiency of the algorithm. The performance of the proposed algorithm is observed based on comparative experiments with notable benchmark problems and computationally expensive wastewater treatment processes (WWTP). The experimental results prove that the proposed algorithm keeps the trade-off between not only the global and local search, but also the evaluation cost and convergence, which is suitable for computationally expensive optimisation problems. Inderscience Publishers - linking academia, business and industry through research

Title: Adaptive surrogate-based swarm intelligence algorithm and its application in wastewater treatment processes

Authors: Jing Jie; Rui Dai; Hui Zheng; Miao Zhang; Lu Lu

Addresses: School of Automation and Electrical Engineering, Zhejiang University of Science and Technology, Liuhe Road 318#, Hangzhou Zhejiang 310023, China ' School of Automation and Electrical Engineering, Zhejiang University of Science and Technology, Liuhe Road 318#, Hangzhou Zhejiang 310023, China ' School of Automation and Electrical Engineering, Zhejiang University of Science and Technology, Liuhe Road 318#, Hangzhou Zhejiang 310023, China ' School of Automation and Electrical Engineering, Zhejiang University of Science and Technology, Liuhe Road 318#, Hangzhou Zhejiang 310023, China ' School of Automation and Electrical Engineering, Zhejiang University of Science and Technology, Liuhe Road 318#, Hangzhou Zhejiang 310023, China

Abstract: To solve the computationally expensive optimisation problems effectively, a surrogate-based swarm intelligence algorithm is presented. A global surrogate model and a local one are respectively built to approximately evaluate the fitness values of the individuals instead of the accurate function during the evolution process. Following that, the control strategy of swarm evolution and the surrogate management are designed in detail to improve the efficiency of the algorithm. The performance of the proposed algorithm is observed based on comparative experiments with notable benchmark problems and computationally expensive wastewater treatment processes (WWTP). The experimental results prove that the proposed algorithm keeps the trade-off between not only the global and local search, but also the evaluation cost and convergence, which is suitable for computationally expensive optimisation problems.

Keywords: swarm intelligence; surrogate model; particle swarm optimisation; Gaussian process; wastewater treatment processes; WWTP.

DOI: 10.1504/IJBIC.2023.130550

International Journal of Bio-Inspired Computation, 2023 Vol.21 No.2, pp.81 - 93

Received: 07 Sep 2021
Received in revised form: 27 Dec 2021
Accepted: 27 Dec 2021
Published online: 27 Apr 2023 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article

Title: Adaptive surrogate-based swarm intelligence algorithm and its application in wastewater treatment processes

Keep up-to-date