1 research outputs found
Particle swarm optimization with state-based adaptive velocity limit strategy
Velocity limit (VL) has been widely adopted in many variants of particle
swarm optimization (PSO) to prevent particles from searching outside the
solution space. Several adaptive VL strategies have been introduced with which
the performance of PSO can be improved. However, the existing adaptive VL
strategies simply adjust their VL based on iterations, leading to
unsatisfactory optimization results because of the incompatibility between VL
and the current searching state of particles. To deal with this problem, a
novel PSO variant with state-based adaptive velocity limit strategy (PSO-SAVL)
is proposed. In the proposed PSO-SAVL, VL is adaptively adjusted based on the
evolutionary state estimation (ESE) in which a high value of VL is set for
global searching state and a low value of VL is set for local searching state.
Besides that, limit handling strategies have been modified and adopted to
improve the capability of avoiding local optima. The good performance of
PSO-SAVL has been experimentally validated on a wide range of benchmark
functions with 50 dimensions. The satisfactory scalability of PSO-SAVL in
high-dimension and large-scale problems is also verified. Besides, the merits
of the strategies in PSO-SAVL are verified in experiments. Sensitivity analysis
for the relevant hyper-parameters in state-based adaptive VL strategy is
conducted, and insights in how to select these hyper-parameters are also
discussed.Comment: 33 pages, 8 figure