A new algorithm, dubbed memory-based adaptive partitioning (MAP) of search space, which is intended to provide a better accuracy/speed ratio in the convergence of multi-objective evolutionary algorithms (MOEAs) is presented in this work. This algorithm works by performing an adaptive-probabilistic refinement of the search space, with no aggregation in objective space. This work investigated the integration of MAP within the state-of-the-art fast and elitist non-dominated sorting genetic algorithm (NSGAII). Considerable improvements in convergence were achieved, in terms of both speed and accuracy. Results are provided for several commonly used constrained and unconstrained benchmark problems, and comparisons are made with standalone NSGAII and hybrid NSGAII-efficient local search (eLS). search space partitioning multiplier step size for a given search space i Pareto front improvement within the last r generations Euclidian distance restricted search space i hypervolume indicator variable importance inverted generational distance hypervolume distance between the Pareto optimal front and an approximated Pareto front interval numbers relative to a same category of variables i partitioning degree number of objective functions memory matrix population size number of variables statistical p-value population Pareto front optimal Pareto front partitioning tendency partitioning vector solution belonging to a restricted search space D _ i vector of decision variables vector of restricted decision variables vector of congruent variables i vector of restricted congruent variables i maximum value in a set of restricted congruent variables x ˜ _ i minimum value in a set of restricted congruent variables x ˜ _ i matrix of restricted solution vectors lower upper 
Memory-based adaptive partitioning (MAP) of search space for the enhancement of convergence in Pareto-based multi-objective evolutionary algorithms