Ant Colony Optimization
Tracking Non-Stationary Optimal Solution by Particle Swarm Optimizer
SNPD-SAWN '05 Proceedings of the Sixth International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing and First ACIS International Workshop on Self-Assembling Wireless Networks
A Generalized Approach to Construct Benchmark Problems for Dynamic Optimization
SEAL '08 Proceedings of the 7th International Conference on Simulated Evolution and Learning
An evaporation mechanism for dynamic and noisy multimodal optimization
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
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This paper presents a novel approach to dealing with sample noise in Particle Swarm Optimization (PSO) by introducing a heterogeneous swarm whose particles have different evaporation factors. So far, previous works have considered only homogeneous swarms in which the evaporation factor is the same across particles. However, choosing a proper factor largely depends on the severity of noise in the optimization problem. If the level of noise cannot be determined a priori, arbitrarily choosing the evaporation factor can lead to rather poor results. This paper shows that heterogeneous swarms are generally better than homogeneous ones in low to medium levels of noise, and also in its absence.