Multi-strategy adaptive particle swarm optimization for numerical optimization

• An innovative strategy of diversity-measurement is developed to evaluate the population distribution.
• An elitist learning strategy (ELS) is implemented in the local optimal regions to help some special particles to jump out of local optimal regions.
• To balance the ability of global exploration and local exploitation, we design an adjusting scheme of Adaptive control of inertia weight w according to the diversity of the population distribution.

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.

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. Fig. 1(a) shows the general distribution among particles. Uniform distribution and extreme distribution are shown in Fig. 1(b) and (c), respectively. To balance the ability of global exploration and local exploitation, we design an adjustment scheme of the inertia weight w according to the strategy of diversity-measurement. 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.Figure optionsDownload as PowerPoint slide