Dynamic Search With Charged Swarms
GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
A hierarchical particle swarm optimizer for noisy and dynamic environments
Genetic Programming and Evolvable Machines
Multi-strategy ensemble particle swarm optimization for dynamic optimization
Information Sciences: an International Journal
Evaporation as a Self-Adaptation Mechanism for PSO
SASO '08 Proceedings of the 2008 Second IEEE International Conference on Self-Adaptive and Self-Organizing Systems
Keeping diversity when exploring dynamic environments
Proceedings of the 2009 ACM symposium on Applied Computing
Evolutionary optimization in uncertain environments-a survey
IEEE Transactions on Evolutionary Computation
Multiswarms, exclusion, and anti-convergence in dynamic environments
IEEE Transactions on Evolutionary Computation
Description and composition of bio-inspired design patterns: the gradient case
Proceedings of the 3rd workshop on Biologically inspired algorithms for distributed systems
Evaporation mechanisms for particle swarm optimization
SEAL'12 Proceedings of the 9th international conference on Simulated Evolution and Learning
Description and composition of bio-inspired design patterns: a complete overview
Natural Computing: an international journal
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Dealing with imprecise information is a common characteristic in real-world problems. Specifically, when the source of the information are physical sensors, a level of noise in the evaluation has to be assumed. Particle Swarm Optimization is a technique that presented a good behavior when dealing with noisy fitness functions. Nevertheless, the problem is still open. In this paper we propose the use of the evaporation mechanism for managing with dynamic multi-modal optimization problems that are subject to noisy fitness functions. We will show how the evaporation mechanism does not require the detection of environment changes and how can be used for improving the performance of PSO algorithms working in noisy environments.