A Novel Hybrid Algorithm Based on Baldwinian Learning and PSO

In the paper, a novel hybrid algorithm based on Baldwinian learning and PSO (BLPSO) is proposed to increase the diversity of the particles and to prevent premature convergence of PSO. Firstly, BLPSO adopts the Baldwinian operator to simulate the learning mechanism among the particles and employs the information of the swarm to alter the search space adaptively. Secondly, a mutation operation is introduced to make the particles leap the local optimum and enhance the chance to find out the global optimum. Finally, the proposed BLPSO is used to solve some complex optimization problems, the experiment results illustrate the efficiency of the proposed method.