Multi-swarm Optimization in Dynamic Environments

By Tim M. Blackwell, J. Branke. EvoWorkshops, 2004

422 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Theory and Mathematics

View Paper: https://www.semanticscholar.org/paper/2d9b42d3353a27e026a8cad292c0c7cc2c5bff5b

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Yaochu Jin
2. Shengxiang Yang
3. Changhe Li
4. Ponnuthurai Nagaratnam Suganthan
5. J. Branke
6. Christian Blum
7. Jing J. Liang
8. Michalis Mavrovouniotis

In Computational Theory and Mathematics

1. Shengxiang Yang
2. Yaochu Jin
3. Changhe Li
4. Ponnuthurai Nagaratnam Suganthan
5. J. Branke
6. Christian Blum
7. Michalis Mavrovouniotis
8. Tim M. Blackwell

Citations in the corpus

• Parallel metaheuristics: recent advances and new trends (2012, background): “Some recent examples are Blackwell and Branke ( 2004 ) , Du and Li ( 2008 ) , Khouadjia et al .”
• 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.”
• Evolutionary optimization in uncertain environments-a survey (2005, background): “Finally, in [25] and [153], multiswarms have been proposed,”
• Swarm Intelligence in Optimization (2008, methodology): “also demonstrated that the quantum particle model in [11] can be incorporated into SPSO to improve its optima-tracking performance for the moving peaks problem [24].”
• Dynamic multi-swarm particle swarm optimizer with local search (2005, background): “Except these local PSO variants, some variants that use multi-swarm [8], subpopulation [9] can also be included in the local version PSOs if we treat the sub-groups as special neighborhood structures.”
• A survey of swarm intelligence for dynamic optimization: Algorithms and applications (2017, methodology): “[140, 141, 142, 143, 144, 145, 146, 147, 148, 98, 149, 99, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 97, 164, 102, 165, 166, 101, 167, 168] [169, 170, 171, 172] [173, 174, 175] [176, 177] [178, 179, 180, 181, 182, 183]”
• A Clustering Particle Swarm Optimizer for Locating and Tracking Multiple Optima in Dynamic Environments (2010, methodology): “The atomic swarm approach has been adapted to track multiple optima simultaneously with multiple swarms in dynamic environments by Blackwell and Branke [ 4 ], [5].”
• Particle Swarm Optimization: A survey of historical and recent developments with hybridization perspectives (2018, methodology): “[180], rPSO by Bird and Li [181], Dynamic SPSO by Parrot and Li [182] and the lbest PSO with Ring Topology by Li [183] are some of the well-known approaches used in such environments.”
• Multiswarms, exclusion, and anti-convergence in dynamic environments (2006, methodology): “For a more extensive treatment of PSO techniques, the reader is referred to [17], [19], and [20].”