Simulated annealing for many objective optimization problems
Eduardo Ramon Morales Ferreira, Benjamı́n Barán · 2016
The difficulty to solve many objective optimization problems (MaOP) with well-established Multi-objective Evolutionary Algorithms as NSGA-II (Non-dominated Sorting Genetic Algorithm-II) motivates this work to develop a new alternative for solving MaOP problems. Thus, this paper proposes a novel variant of Simulated Annealing (SA) as an alternative to solve MaOP problems, combining also the proposed SA with clustering reduction techniques and tabu search. A comparative analysis between the proposed algorithm and the reference algorithm NSGA-II is presented using the recognized test set DTLZ. Experimental results using different performance metrics prove the advantages of the proposed algorithm over a well-established state of the art algorithm as NSGA-II.