Particle swarm optimization(PSO) suffers from the premature convergence problem in complex optimization
problems. To solve this problem, this paper analyzes PSO algorithm from cognitive psychology and proposes a creative
thinking(CT) based PSO algorithm(CTPSO). Based on the four stages model in CT process, a framework of CTPSO is
designed, and the evolution model is adapted, which includes a CT model besides the memory model, cognitive model
and social model in standard PSO to improve the optimization capability of particles. CTPSO is applied to some wellknown
benchmarks, and experimental results show that CTPSO possesses more powerful global search capabilities, better
convergence rate and robustness, meanwhile it does not introduce new parameters and computational complexity.