Standing on a psychological point of view, a particle swarm optimization algorithm with extended memory
(PSOEM) is presented for the problem that particles often lost their way when applying the standard particle swarm
optimization(PSO) algorithm to optimization multidimensional functions. The extended memory is introduced to store
each particle’s historical information and a parameter is employed to describe the importance of extended memory as well.
Stability region of its deterministic version in a dynamic environment is analyzed by means of the classic discrete control
theory. Because PSO with extended memory(PSOEM) and PSO are homologous but heterogeneous in structure, the specialty
of PSOEM is that it can integrate with numerous existing improved PSO algorithms and combine respective advantages.
Results of simulation on benchmark functions show the effectiveness of the proposed algorithm.