PARTICLE SWARM OPTIMIZATION BASED ON TENT MAP AND LOGISTIC MAP
D. Tian, Tian-xu ZHAO · Journal of Shaanxi University of Science & Technology · 2010
Particle swarm optimization (PSO) is a population-based stochastic optimization originating from artificial life and evolutionary computation. PSO, however, has a feature of un-stability during its running, and like other evolutionary algorithms, has a tendency to get stuck in local optimal solutions during the search process. So, two improved particle swarm optimization are proposed in this paper, which are PSO with an initial population of tent map solutions and Gaussian mutation based on maximal focus distance (Tent-PSO) and PSO with an initial population of logistic map solutions and Gaussian mutation based on maximal focus distance (Logistic-PSO), respectively. Simulation results on two benchmark functions illustrate that the PSO proposed in this paper is feasible and efficient.