ABSTRACT
The generational distance convergence metric α measures the degree of closeness between the sets of the nondominated solutions and the true Pareto-optimal solutions. To verify the effectiveness of jumping gene (JG) in multiobjective optimization, the most straightforward approach is to use some well-known benchmark test functions for evaluating its performance. The true Pareto-optimal fronts of these test functions should be obtainable with various characteristics. The success of the JG relies on three important parameters: jumping rate, number of transposons, and length of transposon. Any variation of these parameters can affect its performance on convergence and diversity. However, due to the huge number of schemata in a practical design problem, only low-order schemata can be data logged. In the following, this is demonstrated by applying a multiobjective genetic algorithm (MOGA) with or without JG onto a practical design example to demonstrate a full case.
