Fitness-distance-ratio based particle swarm optimization

By Thanmaya Peram, Kalyan Veeramachaneni, C. Mohan. Proceedings of the 2003 IEEE Swarm Intelligence Symposium. SIS'03 (Cat. No.03EX706), 2003

507 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Theory and Mathematics

View Paper: https://www.semanticscholar.org/paper/f3ff6097f1bcf44a24877fd95579167d4e3d992a

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Citations in the corpus

Particle swarm optimization algorithm: an overview (2017, methodology): “Peram et al. (2003) developed a new PSO algorithm based on ﬁtness–distance ratio (FDR-PSO) using the interaction of the neighbors.”
Novel meta-heuristic bald eagle search optimisation algorithm (2019, background): “…as many algorithms modiﬁed on the PSO algorithms, such as comprehensive learning particle swarm optimiser (CLPSO) (Liang et al. 2006) and ﬁtness-distance-ratio-based particle swarm optimisation (FDR-PSO) (Peram et al. 2003) and ensemble particle swarm optimiser (EPSO) (Lynn and Suganthan 2017).”
Self regulating particle swarm optimization algorithm (2015, methodology): “A novel algorithm using the personal best, global best and another particle’s personal best with a maximum fitness-to-distance ratio for updating a particle’s velocity was introduced in [55].”
Dynamic multi-swarm particle swarm optimizer (2005, background): “…Version of PSO with inertia weight (PSO_w_local) Local Version of PSO with constriction factors (PSO_cf_local) [4] Unified Particle Swarm Optimization (UPSO) [15] Fitness-distance-ratio based particle swarm optimization (FDR–PSO) [7] Cooperative PSO (CPSO-H) [16] Dynamic multi-swarm PSO (DMS-PSO)”
Orthogonal Learning Particle Swarm Optimization (2009, background): “[38] have suggested updating the particle velocity by three learning components, two of which are the personal best position and the globally best position, and the other is the particle which has a higher fitness and is nearer to the current particle with a maximal fitness-to-distance ratio.”
Diversity enhanced particle swarm optimization with neighborhood search (2013, background): “Peram et al. [41] developed the ﬁtness-distance-ratio based PSO (called FDR-PSO), in which each particle is attracted towards the best previous positions visited by its neighbors.”
Comprehensive Learning Particle Swarm Optimization Algorithm With Local Search for Multimodal Functions (2019, unknown): “L-PSO [34], DNLPSO [29], HCLPSO [30], SRPSO [47], DMeSR-PSO [23], UPSO [48], FIPS [49], FDR-PSO [50], and CPSO-H [51].”
A review on particle swarm optimization algorithm and its variants to clustering high-dimensional data (2013, background): “The direction in which each component of particle position needs to be changed is determined by using the ratio of relative fitness and the distance of other particle (Peram et al. 2003).”
Self-adaptive learning based particle swarm optimization (2011, background): “In the first section, we stated that the performance of previous PSO variants on ill-conditioned problems is very poor [19,28,36,41].”
Comprehensive learning particle swarm optimizer for global optimization of multimodal functions (2006, background): “Veeramachaneni et al. developed the ﬁtness-distance-ratio-based PSO (FDR-PSO) with near neighbor interactions [20].”