EVOLUTIONARY ALGORITHM BASED ON SIMULATED ANNEALING FOR THE MULTI-OBJECTIVE OPTIMIZATION OF COMBINATORIAL PROBLEMS
Elías D. Niño-Ruiz, Henry David Nieto Parra, Anangelica Isabel Chinchilla Camargo · Redalyc (Universidad Autónoma del Estado de México) · 2013
"This paper states a novel hybrid-metaheuristic based on the Theory of Deterministic Swapping, Theory of Evolution and Simulated Annealing Meta-heuristic for the multi-objective optimization of combinatorial problems. The proposed algorithm is named EMSA. It is an improvement of MODS algorithm. Unlike MODS, EMSA works using a search direction given through the assignation of weights to each function of the combinatorial problem to optimize. Also, in order to avoid local optimums, EMSA uses crossover strategy of Genetic Algorithm. Lastly, EMSA is tested using well know instances of the Bi-Objective Traveling Salesman Problem (TSP) from TSPLIB. Its results were compared with MODS Metaheuristic (its precessor). The comparison was made using metrics from the specialized literature such as Spacing, Generational Distance, Inverse Generational Distance and Non-Dominated Generation Vectors. In every case, the EMSA results on the metrics were always better and in some of those cases, the superiority was 100%."