PARMODS: A Parallel Framework for MODS Metaheuristics


  • Elias D. Nino Ruiz Universidad del Norte Computer Science Department Colombia, Barranquilla
  • Stella Miranda Universidad del Norte Computer Science Department Colombia, Barranquilla
  • Carlos J. Ardila Universidad del Norte Computer Science Department Colombia, Barranquilla
  • Wilson Nieto Universidad del Norte Computer Science Department Colombia, Barranquilla


MODS, Combinatorial Optimization, Parallel Framework


In this paper, we propose a novel framework for the parallel solution of combinatorial problems based on MODS theory (PARMODS) This framework makes use of metaheuristics based on the Deterministic Swapping (MODS) theory. These approaches represents the feasible solution space of any combinatorial problem through a Deterministic Finite Automata. Some of those methods are the Metaheuristic Of Deterministic Swapping (MODS), the Simulated Annealing Deterministic Swapping (SAMODS), the Simulated Annealing Genetic Swapping (SAGAMODS) and the Evolutionary Deterministic Swapping (EMODS) Those approaches have been utilized in different contexts such as data base optimization, operational research [1—3, 8] and multi-objective optimization. The main idea of this framework is to exploit parallel computation in order to obtain a general view of the feasible solution space of  any combinatorial optimization problem. This is, all the MODS methods are used in a unique general optimization process. In parallel, each instance of MODS explores a different region of the solution space. This allows us to explore distant regions of the feasible solution which could not be explored making use of classical (sequential) MODS implementations. Some experiments are performed making use of well-known TSP instances. Partial results shows that PARMODS provides better solutions than sequential MODS based implementations.


Anonnimus (1964); Operational research studies in inventory sequencing simulation, Production Engineer, 43(9):437-438, DOI: 10.1049/tpe.1964.0060.

Anonnimus (1964); Operational research studies. project a-inventory. Production Engineer, 43(9):438-447, DOI: 10.1049/tpe:19640061.

Junyi Chen and Pingyuan Xi (2010); Simulation and application on modern operational research. In Computer and Automation Engineering (ICCAE), 2010 The 2nd International Conference on, 4: 118-121.

Elias D. Ni-o (2012); Real-World Applications of Genetic Algorithms, chapter Evolutionary Algorithms Based on the Automata Theory for the Multi-Objective Optimization of Combinatorial Problems. InTech, Oxford, 2012. Book edited by Olympia Roeva.

Elias D. Nino, Carlos J. Ardila, and Anangelica Chinchilla (2012); A novel, evolutionary, simulated annealing inspired algorithm for the multi-objective optimization of combinatorial problems. Procedia Computer Science, 9(0):1992 - 1998.

Elias D. Nino-Ruiz (2012); Evolutionary Algorithm based on the Automata Theory for the Multi-objective Optimization of Combinatorial Problems. International Journal Of Computers Communication & Control, 7(5):916-923.

Miguel Rodríguez, Daladier Jabba, Elias D. Ni-o, Carlos J. Ardila, and Yi-Cheng Tu (2013); Automata theory based approach to the join ordering problem in relational database systems. In Markus Helfert, Chiara Francalanci, and Joaquim Filipe, editors, DATA, pages 257-265. SciTePress.

Li Zhengfeng and Ye Jinfu (2010); Study on the evolutionary mechanism from operational research activities to sustainable competitive advantage. In Intelligent Computation Technology and Automation (ICICTA), 2010 International Conference on, 3: 580-584.



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