Hybrid particle swarm optimizer with tabu strategy for global numerical optimization

Particle swarm optimizer (PSO) is a population- based evolutionary algorithm which is widely adopted due to its simple implementation and fast convergence. But, when optimizing complex problems, PSO may lead to premature convergence. In this paper, inspired by the core idea of the tabu search algorithm, we incorporate the tabu strategy and propose a revised PSO with a view to increase population diversity and to reduce the repeated attractions by local minima. The two-stage searching strategy offers a good trade-off between exploration and exploitation and meanwhile, experimental results show significant performance improvements on seven benchmark functions.