Scalability of multiobjective genetic local search to many-objective problems: Knapsack problem case studies

It is well-known that Pareto dominance-based evolutionary multiobjective optimization (EMO) algorithms do not work well on many-objective problems. This is because almost all solutions in each population become non-dominated with each other when the number of objectives is large. That is, the convergence property of EMO algorithms toward the Pareto front is severely deteriorated by the increase in the number of objectives. Currently the design of scalable EMO algorithms is a hot issue in the EMO community. In this paper, we examine the scalability of multiobjective genetic local search (MOGLS) to many-objective problems using a hybrid algorithm of NSGA-lI and local search. Multiobjective knapsack problems with 2, 4, 6, 8, and 10 objectives are used in computational experiments. It is shown by experimental results that the performance of NSGA-lI is improved by the hybridization with local search independent of the number of objectives in the range of 2 to 10 objectives.