Improved ACLPSO Algorithm for Automatic Parameter Optimization

The ACLPSO algorithm, based on dimension specification intervals, adaptively tunes maximum velocity, inertia weight, acceleration coefficient, and learning probability for each dimension. It has demonstrated excellent performance in benchmark function tests involving both single-modal and multi-modal functions. However, to obtain the global optimal or approximate optimal solutions for all executed benchmark functions, it is necessary to manually set appropriate values for the maximum velocity coefficient s and the learning probability coefficient v during the operation of the ACLPSO algorithm. This study introduces an automatic approach to assign values to s and v, relying on function iteration count and test function fitness convergence. This enhancement enables the improved ACLPSO algorithm to directly derive the global optimal or approximate optimal solutions for all benchmark functions, eliminating the need for manual parameter tuning.