3 research outputs found
Handling boundary constraints for numerical optimization by particle swarm flying in periodic search space
The periodic mode is analyzed together with two conventional boundary
handling modes for particle swarm. By providing an infinite space that
comprises periodic copies of original search space, it avoids possible
disorganizing of particle swarm that is induced by the undesired mutations at
the boundary. The results on benchmark functions show that particle swarm with
periodic mode is capable of improving the search performance significantly, by
compared with that of conventional modes and other algorithms.Comment: Congress on Evolutionary Computation, 2004. CEC2004. Volume: 2, On
page(s): 2307- 2311 Vol.
Towards a Better Understanding of the Local Attractor in Particle Swarm Optimization: Speed and Solution Quality
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. As a compromise, we propose and evaluate a hybrid PSO which
switches off its local attractors at a certain point in time. The effects
mentioned can also be observed by measuring the potential of the swarm