一种基于创造性思维的粒子群优化算法

Particle swarm optimization algorithm based on creative thinking

针对粒子群优化(PSO)算法在复杂问题求解中出现的早熟收敛问题,从认知心理学角度进行分析,将创造性思维(CT)引入PSO算法,提出一种基于创造性思维的PSO算法(CTPSO).基于CT过程的"四阶段"模型,构建了算法框架,改进了速度更新公式,在粒子个体的惯性、个体认知和社会能力的基础上增强CT能力,以提升其整体寻优性能.典型测试函数的运行结果表明,该算法具有较强的全局搜索能力,收敛速度快,算法稳定性好,且未增加新的参数和计算复杂度.

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 well- known 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 comoutational comolexity.