Debasish Ghose

Biomedical Engineering · Swarm Intelligence · 2004–2009

A researcher in the Science corpus: 3 papers; trusts 8 researchers and is trusted by 17 researchers here, as inferred from the citation graph.

Subfields

Whom Debasish Ghose trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

  1. Sibylle D. Müller
  2. Marco Dorigo
  3. Krishnanand N. Kaipa
  4. Jarno Marchetto
  5. James Kennedy
  6. Luca Maria Gambardella
  7. Thomas Stützle
  8. Stefano Airaghi

Who trusts Debasish Ghose overall

  1. Rafael Stubs Parpinelli
  2. M. N. Ab Wahab
  3. Kenneth Sörensen
  4. Heitor Silvério Lopes
  5. Krishnanand N. Kaipa
  6. Lucija Brezočnik
  7. Ali Wagdy Mohamed
  8. Yudong Zhang
  9. Samia Nefti-Meziani
  10. Arpan Kumar Kar

Papers from Debasish Ghose's perspective

Ordered by how strongly the researchers Debasish Ghose trusts endorse each paper. Same corpus, a different order for every researcher.

  1. Swarm Intelligence: From Natural to Artificial Systems
  2. Ant colony system: a cooperative learning approach to the traveling salesman problem
  3. On the computation of all global minimizers through particle swarm optimization (IEEE Transactions on Evolutionary Computation)
  4. Distributed odor source localization
  5. A short convergence proof for a class of ant colony optimization algorithms (IEEE Transactions on Evolutionary Computation)
  6. Detection of multiple source locations using a glowworm metaphor with applications to collective robotics (Proceedings 2005 IEEE Swarm Intelligence Symposium, 2005. SIS 2005.)
  7. Stereotyping: improving particle swarm performance with cluster analysis (Proceedings of the 2000 Congress on Evolutionary Computation. CEC00 (Cat. No.00TH8512))
  8. Optimization based on bacterial chemotaxis (IEEE Transactions on Evolutionary Computation)
  9. Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence
  10. Differential Evolution – A Simple and Efficient Heuristic for global Optimization over Continuous Spaces

Their papers