A PSO algorithm based on group history experience

Particle swarm optimization groups adjust the search strategy to obtain evolution by fully sharing information. Rational utilize of the group information also determine the efficiency and performance of particle swarm algorithm. The group historical experience particle swarm optimization (GHEPSO) is proposed, particles are not influenced only by the group optimal position of the current generation time and by their historical optimal position, but also by the group optimal position of previous generation time at the same time. This algorithm more fully uses the group experience information than basic PSO algorithm. The performance of the algorithm is analyzed through several typical test functions, comparing this algorithm with basic particle group algorithm. The result shows that GHEPSO is better to solve the problem of multi-modal function than the basic PSO. And the optimized effect will be more improved if GHEPSO, MPSO and TVAC can be combined together.