LAUSR

In this paper, an approach that incorporates a turbulence mechanism and a circular elimination strategy is presented to strengthen the performance of multi-objective particle swarm optimization (MOPSO). For convergence enhancement, the turbulence mechanism derived from bacteria quorum sensing behavior is introduced to MOPSO to preserve the swarm diversity. Meanwhile, the circular elimination strategy is used to select particles for next iteration for better distribution of the Pareto-optimal solutions. The improved MOPSO algorithm has been tested on a set of benchmark functions and compared with representative multi-objective optimization algorithms. Simulation results illustrate that the algorithm outperforms the other algorithms on convergence while keep good spread performance, and could be used as an effective global optimization tool.