Multi-strategy adaptive particle swarm optimization for numerical optimization

To search the global optimum across the entire search space with a very fast convergence speed, we propose a multi-strategy adaptive particle swarm optimization (MAPSO). MAPSO develops an innovative strategy of diversity-measurement to evaluate the population distribution, and performs a real-time alternating strategy to determine one of two predefined evolutionary states, exploration and exploitation, in each iteration. During iterative optimization, MAPSO can dynamically control the inertia weight according to the diversity of particles. Moreover, MAPSO introduces an elitist learning strategy to enhance population diversity and to prevent the population from possibly falling into local optimal solutions. The elitist learning strategy not only acts on the globally best particle, but also on some special particles that are very near to the globally best particle. The aforementioned features of MAPSO have been comprehensively analyzed and tested on eight benchmark problems and a standard test image. Experimental results show that MAPSO can substantially enhance the ability of PSOs to jump out of the local optimal solutions and significantly improve the search efficiency and convergence speed.