An asynchronous MOPSO for multi-objective optimization problem

This paper presents a multi-objective particle swarm optimization with asynchronous update (AS-MOPSO). That is, Pareto front is immediately evaluated whenever a particle in the swarm updates, a subsequent particle in the swarm regulates its position partly based on information up to current iteration, and partially depending on previous message. To evaluate the features of the proposed algorithm, examples of multiple objective optimization (MOO) were tested. Results indicated advantages of AS-MOPSO in dealing with MOO problems, compared to MOPSO with synchronous update.