MOSCA/D: Multi-objective Scientific Algorithms Based on Decomposition
Decomposition; multi-objective multidimensional knapsack problem; scientific
algorithms
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. MOSCA/D uses the
concept of theme to direct the computational effort of the search to promising regions
of the objective space, fixing different decision variables in each iteration. A probabilistic
model based on the TF-IDF statistic assists the choice of such variables. Computational
experiments applied MOSCA/D to 16 instances of the multi-objective multidimensional
knapsack problem (MOMKP) with up to 8 objectives. The results were compared to
NSGA-II, SPEA2, MOEA/D, MEMOTS, 2PPLS, MOFPA and HMOBEDA, covering three classical multi-objective algorithms, two state of the art algorithms for the problem
and two most recently published algorithms for the problem, respectively. Statistical tests
showed evidence that MOSCA/D can compete with other consolidated approaches from
literature and can now be considered the new state of the art algorithm for the MOMKP
in instances with more than two objectives, considering the hypervolume and epsilon
quality indicators.