Sabine Helwig

Learn More
Many practical optimization problems are constrained, and have a bounded search space. In this paper, we propose and compare a wide variety of bound handling techniques for particle swarm optimization. By examining their performance on flat landscapes, we show that many bound handling techniques introduce a significant search bias. Furthermore, we compare(More)
Particle swarm optimization (PSO) is a nature-inspired technique originally designed for solving continuous optimization problems. There already exist several approaches that use PSO also as basis for solving discrete optimization problems, in particular the Traveling Salesperson Problem (TSP). In this paper, (i) we present the first theoretical analysis of(More)
We propose a generic, hybrid constraint handling scheme for particle swarm optimization called Heterogeneous Constraint Handling. Inspired by the notion of social roles, we assign different constraint handling methods to the particles, one for each social role. In this paper, we investigate two social roles for particles, ‘self’ and(More)
Particle swarm optimization (PSO) is a natureinspired technique for solving continuous optimization problems. For a fixed optimization problem, the quality of the found solution depends significantly on the choice of the algorithmic PSO parameters such as the inertia weight and the acceleration coefficients. It is a challenging task to choose appropriate(More)
Particle swarm optimization (PSO) is an optimization approach from the field of artificial intelligence. A population of so-called particles moves through the parameter space defined by the optimization problem, searching for good solutions. Inspired by natural swarms, the movements of the swarm members depend on own experiences and on the experiences of(More)
In the cycle of Algorithm Engineering, the design phase opens after the modeling phase. We may assume that the algorithmic task to be performed is well understood, i. e., that the desired input-output relation is speci ed, and an agreement has been reached as to what makes a solution to the problem a good solution. These questions must be settled in(More)
Particle swarm optimization (PSO) algorithms have gained increasing interest for dealing with continuous optimization problems in recent years. Often such problems involve boundary constraints. In this case, one has to cope with the situation that particles may leave the feasible search space. To deal with such situations different bound handling methods(More)