Convergence Enhanced Multi-objective Particle Swarm Optimization with Introduction of Quorum-Sensing Inspired Turbulence


Enhancing the convergence property is one of the main goals to achieve when designing a multi-objective particle swarm optimization (MOPSO) algorithm. To promote convergence, a turbulence mechanism derived from the bacteria quorum sensing behavior is introduced and a novel MOPSO (MOPSO-QSIT) is proposed. The inspired turbulence mechanism takes into effect only if the whole current population' velocities are rather small (less than a predefined threshold), which enables to maintain the swarm diversity and avoids declining the swarm evolution. The MOPSO-QSIT algorithm has been tested on a set of benchmark functions and compared with other multiobjective optimization algorithms that are representative of the state-of-the-art. Simulation results illustrate that the proposed algorithm possesses the best convergence performance while keep good diversity performance, and is a competitively effective global optimization tool.