A novel multi-swarm particle swarm optimization with dynamic learning strategy

Abstract In the paper, we proposed a novel multi-swarm particle swarm optimization with dynamic learning strategy (PSO-DLS) to improve the performance of PSO. To promote information exchange among sub-swarms, the particle classification mechanism advocates that particles in each sub-swarm are classified into ordinary particles and communication particles with different tasks at each iteration. The ordinary particles focus on exploitation under the guidance of the local best position in its sub-swarm, while the communication particles with dynamic ability that focus on exploration under the guidance of a united local best position in a new search region promote information to be exchanged among sub-swarms. Moreover the strategy sets a dynamic control mechanism with an increasing parameter p for implementing the classification operation, which provides ordinary particles an increasing sense of evolution into communication particles during the searching process. A simple case of analysis on searching behavior supports its remarkable impact on maintaining the diversity and searching a better solution. Experimental results on 15 function problems of CEC 2015 for 10 and 30 dimensions also demonstrate its promising effectiveness in solving complex problems statistically comparing to other algorithms. What's more, the computational times reveal the subtle design of PSO-DLS.