Adaptively Choosing Neighbourhood Bests Using Species in a Particle Swarm Optimizer for Multimodal Function Optimization
By Xiaodong Li. Annual Conference on Genetic and Evolutionary Computation, 2004
296 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Computational Theory and Mathematics
View Paper: https://www.semanticscholar.org/paper/bded95a4d11844b66927e072344f485daa921d5f
How researchers see this paper
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In Artificial Intelligence
- Shengxiang Yang
- Changhe Li
- Christian Blum
- Michalis Mavrovouniotis
- Xiaodong Li
- Tim M. Blackwell
- J. Branke
- Sanchita Basak
In Computational Theory and Mathematics
- Shengxiang Yang
- Changhe Li
- Christian Blum
- Michalis Mavrovouniotis
- Tim M. Blackwell
- J. Branke
- Xiaodong Li
- Sanchita Basak
Citations in the corpus
- Multiswarms, exclusion, and anti-convergence in dynamic environments (2006, methodology): “Unless stated otherwise, the parameters have been set as follows: the search space has five dimensions , there are peaks, the peak heights vary randomly in the interval [ 30 , 70], and the peak width parameters vary randomly within [1, 12].”
- Particle Swarm Optimization: A survey of historical and recent developments with hybridization perspectives (2018, methodology): “A speciation-based PSO [173] was developed keeping in mind the classification of particles within a threshold radius from the neighborhood best (also known as the seed), as those belonging to a particular species.”
- Swarm Intelligence in Optimization (2008, methodology): “With a similar aim to locate and track multiple peaks in a dynamic environment, Parrott and Li in [101, 135] proposed a species-based PSO (SPSO) incorporating a speciation algorithm first proposed by Pétrowski [139].”
- A survey of swarm intelligence for dynamic optimization: Algorithms and applications (2017, methodology): “The global best model is normally used in multi-swarm based algorithms [97, 98, 99], while the local best model is commonly used in algorithms with a single swarm [100, 101, 102].”
- A Clustering Particle Swarm Optimizer for Locating and Tracking Multiple Optima in Dynamic Environments (2010, methodology): “Parrott and Li developed a speciation-based PSO (SPSO) [30], [ 23 ] which dynamically adjusts the number and size of swarms by constructing an ordered list of particles, ranked according to their fitness, with spatially close particles joining a particular species.”
- Niching Without Niching Parameters: Particle Swarm Optimization Using a Ring Topology (2010, methodology): “Nichin g methods have also been incorporated into PSO variants to enhance their ability to handle multimodal optimization pr oblems including NichePSO [14] and SPSO [15], [16].”