Towards a Better Understanding of the Local Attractor in Particle Swarm Optimization: Speed and Solution Quality
Department of Computer Science
University of Erlangen-Nuremberg, Germany
Particle Swarm Optimization (PSO) is a popular nature-inspired meta-heuristic for
solving continuous optimization problems. Although this technique is widely used,
the understanding of the mechanisms that make swarms so successful is still limited.
We present the first substantial experimental investigation of the influence of the
local attractor on the quality of exploration and exploitation. We compare in detail
classical PSO with the social-only variant where local attractors are ignored.
To measure the exploration capabilities, we determine how frequently both variants
return results in the neighborhood of the global optimum. We measure the quality of
exploitation by considering only function values from runs that reached a search point
sufficiently close to the global optimum and then comparing in how many digits such
values still deviate from the global minimum value. It turns out that the local
attractor significantly improves the exploration, but sometimes reduces the quality
of the exploitation. The effects mentioned can also be observed by measuring th
potential of the swarm.
Proc. 3rd International Conference on Adaptive and Intelligent Systems
(ICAIS); pp. 90-99, 2014.