9 citations · 28 across the 6 of their papers we have counts for
6 papers
Self-adaptive Potential-based Stopping Criteria for Particle Swarm Optimization
Bernd Bassimir, Manuel Schmitt, Rolf Wanka
We study the variant of Particle Swarm Optimization (PSO) that applies random velocities in a dimension instead of the regular velocity update equations as soon as the so-called po…
Exact Markov Chain-based Runtime Analysis of a Discrete Particle Swarm Optimization Algorithm on Sorting and OneMax
Moritz Mühlenthaler, Alexander Raß, Manuel Schmitt +1
Meta-heuristics are powerful tools for solving optimization problems whose structural properties are unknown or cannot be exploited algorithmically. We propose such a meta-heuristi…
Explanation of Stagnation at Points that are not Local Optima in Particle Swarm Optimization by Potential Analysis
Alexander Raß, Manuel Schmitt, Rolf Wanka
Particle Swarm Optimization (PSO) is a nature-inspired meta-heuristic for solving continuous optimization problems. In the literature, the potential of the particles of swarm has b…
Towards a Better Understanding of the Local Attractor in Particle Swarm Optimization: Speed and Solution Quality
Vanessa Lange, Manuel Schmitt, Rolf Wanka
Particle Swarm Optimization (PSO) is a popular nature-inspired meta-heuristic for solving continuous optimization problems. Although this technique is widely used, the understandin…
Particles Prefer Walking Along the Axes: Experimental Insights into the Behavior of a Particle Swarm
Manuel Schmitt, Rolf Wanka
Particle swarm optimization (PSO) is a widely used nature-inspired meta-heuristic for solving continuous optimization problems. However, when running the PSO algorithm, one encount…
Exploiting Independent Subformulas: A Faster Approximation Scheme for #k-SAT
Manuel Schmitt, Rolf Wanka
We present an improvement on Thurley's recent randomized approximation scheme for #k-SAT where the task is to count the number of satisfying truth assignments of a Boolean function…