摘要
针对混沌蚂蚁群优化算法(CASO)容易陷入局部极值和精度低的缺陷,从认知学角度进行分析,将创造性思维(CT)引入CASO算法,提出了一种带创造性思维的混沌蚂蚁群优化算法(CTCASO).基于CT过程的"四阶段"模型,构建了算法框架,改进了位置更新公式,从而使蚂蚁个体在惯性、认知能力的基础上增强了CT能力,提高了蚁群的整体寻优能力.仿真结果表明,所提出的算法搜索能力强、稳定性好,并且未增加新的参数和计算难度.
Chaotic ant swarm optimization （CASO） suffers from premature convergence frequently and low accuracy computation. Therefore, the CASO algorithm is analyzed from cognitive science, and a creative thinking （CT） based CASO （CTCASO） algorithm is proposed. Based on the four stages model in CT process, a framework of the CTCASO algorithm is designed, and the evolution model is adapted, which includes a CT model besides the memory model, and the cognitive model in CASO, to improve the optimization capability of ants. The CTCASO algorithm is applied to some well-known benchmarks, and experimental results show that the CTCASO algorithm possesses more powerful search capabilities and robustness, meanwhile it does not introduce new parameters and computational complexity.
出处
《控制与决策》
EI
CSCD
北大核心
2014年第5期937-940,共4页
Control and Decision
基金
国家自然科学基金项目(61272057
61202434
61170270)
关键词
群智能
混沌蚂蚁群优化算法
创造性思维
基准函数
swarm intelligence
chaotic ant swarm optimization algorithm
creative thinking
benchmark functions
