Algorithm structure optimization by choosing operators in multiobjective genetic local search

An important implementation issue in the design of hybrid evolutionary multiobjective optimization algorithms such as multiobjective genetic local search (MOGLS) is how to combine local search with evolutionary algorithms. It has been demonstrated that the performance of MOGLS strongly depends on the order of global search and local search. A balance between local search and global search also affects its search ability. We can use three ideas for designing high-performance MOGLS algorithms. One idea is to choose one of two options: local search after global search or global search after local search. In general, their appropriate order depends on the problem. Another idea is to use tuned parameter values to appropriately specify their balance. The other idea is to change both their order and the parameter values during the execution of MOGLS. This idea can be implemented by dividing the whole search period into some sub-periods (i.e., dividing all generations into some intervals of generations). The appropriate order and parameter values are assigned to each sub-period. In this paper, we propose off-line algorithm structure optimization for MOGLS. The effectiveness of the proposed idea is examined by computational experiments on a two-objective knapsack problem and a two-objective flowshop scheduling problem. Based on experimental results, we discuss the importance of structure optimization of MOGLS.