The MALLBA project is an effort to develop, in an integrated way, a library of skeletons for combinatorial optimization (including exact, heuristic and hybrid methods) that can deal with parallelism in a user-friendly and, at the same time, efficient manner. Mallba is able to run in isolated machined as well as local and wide area networks (LANs and WANs). The main features of MALLBA are:

• Integration of all the skeletons under the same design principles.
• Facility to switch from sequential to parallel optimization engines. By providing sequential implementations users obtain parallel implementations.
• Cooperation between engines makes possible to provide more powerful hybrid engines.
• Ready to use on commodity machines.
• Flexible and extensible software architecture. New skeletons can easily be added, alternative communication layers can be used, etc.

• Mallba Library

  • Introduction
  • Mallba Architecture
  • Implementation
  • How to install Mallba



• Algorithms

  • Genetic Algorithm (GA)
  • Simulated Annealing (SA)
  • CHC Method
  • Evolution Strategy (ES)
  • Ant Colony Optimization (ACO)
  • GA+SA Hybrid Algorithm
  • Cooperative Local Search (CLS)
  • Particle Swarm Optimization (PSO)



• Examples

  • MAXSAT
  • ONEMAX
  • Sphere Function
  • Rastrigin Function



• Proyect Details




• Appendix: EA parameters

  • Selection Methods
  • Intra-population Operators
  • Inter-population Operators

• Download Mallba v2.0