A modified particle swarm optimization using adaptive strategy

In expert systems, complex optimization problems are usually nonlinear, nonconvex, multimodal and discontinuous. As an efficient and simple optimization algorithm, particle swarm optimization(PSO) has been widely applied to solve various real optimization problems in expert systems. However, avoiding premature convergence and balancing the global exploration and local exploitation capabilities of the PSO remains an open issue. To overcome these drawbacks and strengthen the ability of PSO in solving complex optimization problems, a modified PSO using adaptive strategy called MPSO is proposed. In MPSO, in order to well balance the global exploration and local exploitation capabilities of the PSO, a chaos-based non-linear inertia weight is proposed. Meanwhile, to avoid the premature convergence, stochastic and mainstream learning strategies are adopted. Finally, an adaptive position updating strategy and terminal replacement mechanism are employed to enhance PSO’s ability to solve complex optimization problems in expert systems. 30 complex CEC2017 benchmark functions are utilized to verify the promising performance of MPSO, experimental results and statistical analysis indicate that MPSO has competitive performance compared with 16 state-of-the-art algorithms. The source code of MPSO is provided at https://github.com/lhustl/MPSO .