Authors:
Elias David Nino Ruiz
1
and
Anangelica Isabel Chinchilla Camargo
2
Affiliations:
1
Virginia Polytechnic Institute and State University, United States
;
2
Universidad del Norte, Colombia
Keyword(s):
Combinatorial Optimization, Metaheuristic, Evolutionary Rules, MultiObjective Optimization, Traveling Salesman Problem.
Related
Ontology
Subjects/Areas/Topics:
Applications
;
Artificial Intelligence
;
e-Business
;
Enterprise Information Systems
;
Knowledge Discovery and Information Retrieval
;
Knowledge-Based Systems
;
Logistics
;
Methodologies and Technologies
;
Operational Research
;
Optimization
;
OR in Transportation
;
Pattern Recognition
;
Routing
;
Software Engineering
;
Symbolic Systems
Abstract:
This paper states a novel Evolutionary Metaheuristic based in the Automata Theory for the Multiobjective
Optimization of Combinatorial Problems named EMODS. The proposed algorithm uses the natural selection
theory to explore the feasible solutions space of a Combinatorial Problem. Due to this, local optimums are
avoided. Also, EMODS takes advantage in the optimization process from the Metaheuristic of Deterministic
Swapping to avoid finding unfeasible solutions. The proposed algorithm was tested using well known instances
from the TSPLIB with three objectives. Its results were compared against four Multiobjective Simulated
Annealing inspired Algorithms using metrics from the specialized literature. In every case, the EMODS results on the metrics were always better and in some of those cases, the distance from the Real Solutions was 4%.