Adapting Weighted Aggregation for Multiobjective Evolution Strategies

The conventional weighted aggregation method is extended to realize multi-objective optimization. The basic idea is that systematically changing the weights during evolution will lead the population to the Pareto front. Two possible methods are investigated. One method is to assign a uniformly distributed random weight to each individual in the population in each generation. The other method is to change the weight periodically with the process of the evolution. We found in both cases that the population is able to approach the Pareto front, although it will not keep all the found Pareto solutions in the population. Therefore, an archive of non-dominated solutions is maintained. Case studies are carried out on some of the test functions used in [1] and [2]. Simulation results show that the proposed approaches are simple and effective.