To obtain a full set of Pareto solutions in one single run.

In the field of multi-objective optimization using evolutionary algorithms, conventionally different objectives are aggregated and combined into one objective function by using a fixed weight when more than one objective needs to be optimized. With such a weighted aggregation, only one solution can be obtained in one run. Therefore, according to the present information two methods to change the weights systematically and dynamically during the evolutionary optimization are proposed. One method is to assign uniformly distributed weight to each individual in the population of the evolutionary algorithms. The other method is to change the weight periodically when the evolution proceeds.