An evaporation mechanism for dynamic and noisy multimodal optimization
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Properties of Quantum Particles in Multi-Swarms for Dynamic Optimization
Fundamenta Informaticae
Detecting change in dynamic fitness landscapes
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
A particle swarm optimization based memetic algorithm for dynamic optimization problems
Natural Computing: an international journal
Particle swarm optimization with composite particles in dynamic environments
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Swarm algorithms with chaotic jumps applied to noisy optimization problems
Information Sciences: an International Journal
A new particle swarm optimization for dynamic environments
CISIS'11 Proceedings of the 4th international conference on Computational intelligence in security for information systems
Properties of Quantum Particles in Multi-Swarms for Dynamic Optimization
Fundamenta Informaticae
Computational Intelligence and Neuroscience
Hi-index | 0.00 |
New Particle Swarm Optimization (PSO) methods for dynamic and noisy function optimization are studied in this paper. The new methods are based on the hierarchical PSO (H-PSO) and a new type of H-PSO algorithm, called Partitioned Hierarchical PSO (PH-PSO). PH-PSO maintains a hierarchy of particles that is partitioned into several sub-swarms for a limited number of generations after a change of the environment occurred. Different methods for determining the best time when to rejoin the sub-swarms and how to handle the topmost sub-swarm are discussed. A standard method for metaheuristics to cope with noise is to use function re-evaluations. To reduce the number of necessary re-evaluations a new method is proposed here which uses the hierarchy to find a subset of particles for which re-evaluations are particularly important. In addition, a new method to detect changes of the optimization function in the presence of noise is presented. It differs from conventional detection methods because it does not require additional function evaluations. Instead it relies on observations of changes that occur within the swarm hierarchy. The new algorithms are compared experimentally on different dynamic and noisy benchmark functions with a variant of standard PSO and H-PSO that are both provided with a change detection and response method.