A Distance-Based Locally Informed Particle Swarm Model for Multimodal Optimization

By B. Qu, Ponnuthurai Nagaratnam Suganthan, Swagatam Das. IEEE Transactions on Evolutionary Computation, 2013

376 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Theory and Mathematics

View Paper: https://www.semanticscholar.org/paper/5ebf9b1b4d50838354c9a1e37e7169f33f507728

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. Swagatam Das
2. Yaochu Jin
3. Ponnuthurai Nagaratnam Suganthan
4. Ran Cheng
5. Seyedali Mirjalili
6. Xin-She Yang
7. Francisco Herrera
8. Sankha Subhra Mullick

In Computational Theory and Mathematics

1. Yaochu Jin
2. Swagatam Das
3. Ponnuthurai Nagaratnam Suganthan
4. Ran Cheng
5. Xin-She Yang
6. Seyedali Mirjalili
7. Sankha Subhra Mullick
8. Seyed Mohammad Mirjalili

Citations in the corpus

• Recent advances in differential evolution - An updated survey (2016, methodology): “Index-based topologies [37] have been commonly used while Euclidean distance based topologies [85,153,154,176] have not been frequently used.”
• A Competitive Swarm Optimizer for Large Scale Optimization (2015, background): “Recently, a distance-based locally informed particle swarm optimizer was proposed specifically to tackle multimodal problems in [25].”
• Bio-inspired computation: Where we stand and what's next (2019, background): “tive proposals to multimodal problems using PSO algorithms have gravitated on the use of multi-swarms [176], the induction of Euclideanbased niching [168] or a ring topology in the neighborhoods within the swarm [177].”