MULTIOBJECTIVE PARTICLE SWARM OPTIMIZATION BASED ON DIMENSIONAL UPDATE
For multiobjective particle swarm optimization (MOPSO), two particles may be incomparable, i.e., not dominated by each other. The personal best and the global best for the particle become less optimal, thus the convergence becomes slow. Even worse, an archive of a limited size can not cover the entire region dominated by the Pareto front, the uncovered region can contain unidentifiable nondominated solutions that are not optimal, and thus the precision the algorithm achieves encounters a plateau. Therefore we propose dimensional update, i.e., evaluating the particle's fitness after updating each variable of its position. Separate consideration of the impact of each variable decreases the occurrence of incomparable relations, thus improves the performance. Experimental results validate the efficiency of our algorithm.
particle swarm optimization dimensional update personal best multiobjective optimization problem archive learning