MOSCA/D: Multi-objective Scientific Algorithms based on Decomposition
metaheuristic; multi-objective minimum spanning tree problem; multi-objective multi-dimensional knapsack problem; multi-objective optimization; scientific algorithms; scientific research.
This work presents a multi-objective version of the Scientific Algorithms based on decomposition (MOSCA/D). Such approach is a new metaheuristic inspired by the processes of scientific research to solve multi-objective optimization problems. Computational experiments apply MOSCA/D to the multidimensional multi-objective knapsack problem. The results are compared to MEMOTS and 2PPLS, two state of the art algorithms for the problem. Statistical tests show evidence that MOSCA/D can compete with other consolidated approaches from literature considering the hypervolume quality indicator. This work proposes to expand the found results, to apply MOSCA/D to the multi-objective minimum spanning tree problem and to study the behaviour of different probabilistic models as tools to improve the performance of MOSCA/D.