Low-discrepancy sequence initialized particle swarm optimization algorithm with high-order nonlinear time-varying inertia weight
Initial population particles are generated by the Halton sequence to fill the search space efficiently. 1/?2 order nonlinear function is selected to adjust the time-varying inertia weight. Two acceleration coefficients are set to be constants. Performance of the proposed method is tested by a set of well-known benchmark optimization problems.
low-discrepancy sequence particle swarm optimization 1/?2 order nonlinear function nonlinearly decreasing inertial weight constant acceleration coefficients