摘要
针对旅行商问题,提出了一种带自学习算子的粒子群优化算法,根据旅行商问题及离散量运算的特点,对粒子的位置、速度等量及其运算规则进行了重新定义,为抑制早熟停滞现象,定义了变异速度来保持粒子群的多样性,使用自学习算子来提高算法的局部求精能力,使算法在空间探索和局部求精间取得了较好的平衡,与领域中的其它典型算法进行了仿真比较,结果表明,该算法具有良好的性能。
A particle swarm optimization algorithm with self-learning operator is designed to tackle the traveling salesman problem.Based on the characteristics of the traveling salesman problem and the operations of discrete variables, particle＇s position, velocity and their operation rules are redefined. In order to restrain premature stagnation, a mutation velocity is designed to keep the diversity of particle swarm, and a self-learning operator is defined to improve the algorithm＇s intensification ability. Using those operators, the proposed algorithm can get good balance between exploration and exploitation. The simulation results comparing with typical algorithms from the literature show that it can produce good results.
出处
《计算机工程与设计》
CSCD
北大核心
2007年第2期261-263,266,共4页
Computer Engineering and Design
基金
福建省自然科学基金项目(A0540006)
福建省青年人才科技创新基金项目(2006F3013)
关键词
粒子群优化
旅行商问题
自学习算子
变异速度
组合优化
particle swarm optimization
traveling salesman problem
self-learning operator
mutation velocity
combinatorial optimization
