Hybrid Particle Swarm Optimiser with breeding and subpopulations

By Morten Løvbjerg, T. Rasmussen, Thiemo Krink. 2001

534 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/d16b37a8f383e41537d76b5085a9ba7d0c60920c

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Citations in the corpus

Parallel global optimization with the particle swarm algorithm (2004, methodology): “Although several modifications to the original swarm algorithm have been made to improve performance [12, 13, 14, 15, 16] and adapt it to specific types of problems [6, 17, 18], a parallel version has not been previously implemented.”
Synthesis of Circular Antenna Array Using Crossed Particle Swarm Optimization Algorithm (2006, background): “, neighborhood settings [6], hybrid PSO with breeding and subpopulations [7].”
Dynamic multi-swarm particle swarm optimizer (2005, background): “Except these local versions of PSO, some variants which use multi-swarm [9], subpopulation [10] can also be included in the local version PSOs if we treat the sub-groups as a special neighborhood structure.”
Orthogonal Learning Particle Swarm Optimization (2009, methodology): “Apart from selection [27], crossover [29], and mutation [30] operations that have been adopted from GAs, more other operations have also been utilized.”
A Cooperative approach to particle swarm optimization (2004, methodology): “The term “hybrid” has been used to describe at least three different PSO-based algorithms [9], [30], [ 31 ].”
An analysis of particle swarm optimizers (2002, methodology): “The term “hybrid” has been used to describe at least three different PSO-based algorithms [2, 136, 79].”
An approach to multimodal biomedical image registration utilizing particle swarm optimization (2004, methodology): “One such crossover rule for parents and , , is [32] (8) The velocities and of the children can be updated from the velocities of the parents as follows [32]: The probabilities in (8) can differ for each parameter of .”
Particle Swarm Optimization and Differential Evolution Algorithms: Technical Analysis, Applications and Hybridization Perspectives (2008, methodology): “Simulations were carried out to obtain a comparative performance analysis of the new method with respect to: (a) canonical PSO, (b) PSO-TVIW [18,37], (c) MPSO-TVAC [ 20 ], (d) HPSO-TVAC [20], and (e) classical DE. Thus a total of six algorithms were considered – one new, the other five existing in t”
Fitness-distance-ratio based particle swarm optimization (2003, methodology): “Lovbjerg, Rasmussen, and Krink also proposed in [6] an idea of splitting the population of particles into subpopulations and hybridizing the algorithm borrowing the concepts from Genetic Algorithms.”
Self-Adaptive Learning PSO-Based Deadline Constrained Task Scheduling for Hybrid IaaS Cloud (2014, background): “The particle flies toward a new position according to (13), and each value of the new position should not exceed the range of (13) At the beginning, the position and velocity of each particle in the swarm are initialized randomly.”
Particle swarm optimization algorithm: an overview (2017, methodology): “These hybrid algorithms included introducing various genetic operators to the PSO algorithm, such as selection (Angeline 1998a,b; Lovbjerg et al. 2001), crossover (Angeline 1998b; Chen et al. 2014), mutation (Tsa-farakis et al. 2013) or Cauchy mutation (Wang et al. 2011) to increase the diversity…”
Particle swarm optimization in electromagnetics (2004, background): “Another recent and powerful approach integrates both paradigms into a single algorithm utilizing both evolutionary strategies and swarm intelligence [13], [14].”