Polynomial time approximation schemes for Euclidean traveling salesman and other geometric problems
Journal of the ACM (JACM)
Artificial Immune Systems: A New Computational Intelligence Paradigm
Artificial Immune Systems: A New Computational Intelligence Paradigm
On the harmonious mating strategy through tabu search
Information Sciences: an International Journal - Special issue: Evolutionary computation
Path planning on a cuboid using genetic algorithms
Information Sciences: an International Journal
Runtime analysis of an ant colony optimization algorithm for TSP instances
IEEE Transactions on Evolutionary Computation
Information Sciences: an International Journal
Honey bees mating optimization algorithm for the Euclidean traveling salesman problem
Information Sciences: an International Journal
Revisiting the Foundations of Artificial Immune Systems for Data Mining
IEEE Transactions on Evolutionary Computation
Decomposition-Based Memetic Algorithm for Multiobjective Capacitated Arc Routing Problem
IEEE Transactions on Evolutionary Computation
An evolutionary algorithm for large traveling salesman problems
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A Multilevel Memetic Approach for Improving Graph k-Partitions
IEEE Transactions on Evolutionary Computation
Hi-index | 12.05 |
A general method to reduce computing time for large combinatorial optimization problems by the use of a novel proposal is presented. It is based on reducing the problem complexity by the systematic application of vaccines, it is inspired in the concept of immunization derived from Artificial Immune Systems. The method can be applied practically to any combinatorial problem program solver such as genetic algorithms, memetic algorithms, artificial immune systems, ant colony optimization, the Dantzig-Fulkerson-Johnson algorithm, etc., providing optimal and suboptimal routes outperforming the selected algorithm itself. As a direct consequence of reducing problem complexity, the method provides a means to bring combinatorial optimization open problems that are too big to be treated by known techniques to a tractable point where acceptable solutions can be obtained. To demonstrate the proposed methodology the Traveling Salesman Problem for huge quantity of cities was used, we tested the method with modern evolutionary algorithms and the Concorde program. Comparative experiments that shows the effectiveness of the method are presented.