Abstract
In some studies, local search has been combined with an evolutionary multiobjective optimization
(EMO) algorithm to improve the search ability of the EMO algorithm. Hybridization of EMO
algorithms with local search is often referred to as multiobjective genetic local search (MOGLS). Such a
hybrid algorithm is also called a multiobjective memetic algorithm. Multiobjective memetic algorithms
have higher search ability than pure EMO algorithms. To make the search more efficient, we develop
the problem-dependent local search schemes into a simple multiobjective genetic local search (S-MOGLS)
algorithm which we proposed in the previous work. In most of combinatorial optimization problems, there
exist suitable local search schemes for each problem. These suitable local search schemes can be much
more efficient than evolutionary search. In this paper, to make multiobjective memetic algorithm more efficiently,
we apply the local search schemes suited for multiobjective knapsack problem and multiobjective
flowshop scheduling problem to S-MOGLS algorithms. We examine the effectiveness of the improved local
search schemes through computational experiments.
