A hierarchical particle swarm optimizer for noisy and dynamic environments
Genetic Programming and Evolvable Machines
A Novel Opposition-Based Particle Swarm Optimization for Noisy Problems
ICNC '07 Proceedings of the Third International Conference on Natural Computation - Volume 03
Multi-strategy ensemble particle swarm optimization for dynamic optimization
Information Sciences: an International Journal
Bare bones particle swarm optimization with Gaussian or cauchy jumps
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
A study of particle swarm optimization particle trajectories
Information Sciences: an International Journal
Neighborhood re-structuring in particle swarm optimization
AI'05 Proceedings of the 18th Australian Joint conference on Advances in Artificial Intelligence
Neighborhood topologies in fully informed and best-of-neighborhood particle swarms
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
The particle swarm - explosion, stability, and convergence in amultidimensional complex space
IEEE Transactions on Evolutionary Computation
The fully informed particle swarm: simpler, maybe better
IEEE Transactions on Evolutionary Computation
Information Sciences: an International Journal
Information Sciences: an International Journal
Information Sciences: an International Journal
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In this paper, we investigate the use of some well-known versions of particle swarm optimization (PSO): the canonical PSO with gbest model and lbest model with ring topology, the Bare bones PSO (BBPSO) and the fully informed particle swarm (FIPS) on noisy optimization problems. As far as we know, some of these versions like BBPSO and FIPS had not been previously applied to noisy functions yet. A hybrid approach which consists of the swarm algorithms combined with a jump strategy has been developed for static environments. Here, we focus on investigating the introduction of the jump strategy to the swarm algorithms now applied to noisy optimization problems. The hybrid approach is compared experimentally on different noisy benchmark functions. Simulation results indicate that the addition of the jump strategy to the swarm algorithms is beneficial in terms of robustness.