Papers Related to Global Optimization

Global optimization methods work for finding global optimum (a maximum or minimum) of a problem that may be nondifferentiable, non-linear and non-convex, having many local optima. In the following papers three methods have been elaborated: Particle Swarm method, Differential Evolution method and Host-Parasite Coevolutionary algorithm. Their performance have been evaluated on a fairly large number of difficult test or benchmark functions.

  1. Global Optimization by Differential Evolution and Particle Swarm Methods: Evaluation on Some Benchmark Functions. (download).
  2. Global Optimization By Particle Swarm Method: A Fortran Program. (download).
  3. Some New Test Functions for Global Optimization and Performance of Repulsive Particle Swarm Method. (download).
  4. Performance of Repulsive Particle Swarm Method in Global Optimization of Some Important Test Functions: A Fortran Program.(download).
  5. Repulsive Particle Swarm Method on Some Difficult Test Problems of Global Optimization.(download).
  6. Performance of Differential Evolution and Particle Swarm Methods on Some Relatively Harder Multi-Modal Benchmark Functions.(download).
  7. Global Optimization of Some Difficult Benchmark Functions by Host-Parasite Co-Evolutionary Algorithm.(download).
  8. Multi-species Cuckoo Search Algorithm for Global Optimization. (Link to Cognitive Computation, June 2018).

At this site you will also find (here) the papers that use global optimization methods to estimation in econometrics and statistics. Some of which may interest you.