Nibedita Jagadev

Artificial Intelligence · 2015

A researcher in the Science corpus: 1 paper; trusts 6 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

Whom Nibedita Jagadev trusts

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

  1. Jürgen Teich (in Computational Theory and Mathematics)
  2. Sanaz Mostaghim (in Artificial Intelligence)
  3. Prospero C. Naval Jr. (in Artificial Intelligence)
  4. Carlo R. Raquel (in Artificial Intelligence)
  5. Chanan Singh (in Computational Theory and Mathematics)
  6. Lingfeng Wang (in Computational Theory and Mathematics)

Papers from Nibedita Jagadev's perspective

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

  1. Handling multiple objectives with particle swarm optimization (IEEE Transactions on Evolutionary Computation)
  2. Strategies for finding good local guides in multi-objective particle swarm optimization (MOPSO) (Proceedings of the 2003 IEEE Swarm Intelligence Symposium. SIS'03 (Cat. No.03EX706))
  3. Pareto-optimality approach for flexible job-shop scheduling problems: hybridization of evolutionary algorithms and fuzzy logic (Mathematics and Computers in Simulation)
  4. A MOPSO Algorithm Based Exclusively on Pareto Dominance Concepts (International Conference on Evolutionary Multi-Criterion Optimization)
  5. An effective use of crowding distance in multiobjective particle swarm optimization (Annual Conference on Genetic and Evolutionary Computation)
  6. Evolving dispatching rules using genetic programming for solving multi-objective flexible job-shop problems (Computers & industrial engineering)
  7. An effective hybrid optimization approach for multi-objective flexible job-shop scheduling problems (Computers & industrial engineering)
  8. Using the method of maximizing deviation to make decision for multiindices
  9. A Knowledge-Based Ant Colony Optimization for Flexible Job Shop Scheduling Problems (Applied Soft Computing)
  10. Methods and Applications

Their papers