Many real world optimization problems are dynamic, meaning that their optimal solutions are time-varying. In recent years, an effective approach to address these problemshas beenthe multi-swarmPSO (mPSO).Despitethis, webelievethat there is still room for improvement and, in this contribution we propose two simple strategies to increase the effectiveness of mPSO. The first one faces the diversity loss in the swarm after an environment change; while the second one increases the efficiency through stopping swarms showing a bad behavior. From the experiments performed on the Moving Peaks Benchmark, we have confirmed the benefits of our strategies.
