Surrogate-based adaptive particle swarm optimisation Online publication date: Wed, 07-Aug-2019
by Lei Zhang; Jing Jie; Hui Zheng; Xiaoli Wu; Shiqing Dai
International Journal of Wireless and Mobile Computing (IJWMC), Vol. 17, No. 2, 2019
Abstract: Aiming at the shortcomings of Particle Swarm Optimisation (PSO) in solving complex problems, such as large computation cost and long computation time, the paper proposes a Surrogate-based Adaptive Particle Swarm Optimisation (SAPSO). In the algorithm, PSO carries on the optimisation search through a global exploration population and a local exploitation population. At the same time, a global Gaussian process surrogate and a local one are built based on the history search data to evaluate the two populations approximately. Moreover, some adaptive optimisation strategies have been developed, including Latin Hyper-cube Sampling (LHS) based initial strategy, self-adapting local optimisation, EI sampling-based global optimisation and cooperative searching strategy, which can ensure the balance between the global exploration and the local exploitation validly. The experimental results on benchmark problems show that the proposed algorithm not only can decrease the evaluation costs of the functions validly, but also has good convergence and robustness.
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