中文摘要：

约束问题可以转化为优化问题,针对粒子群优化算法在算法的后期易陷入局部最优的缺点,提出TPSO（禁忌粒子群优化算法）,在算法的前期采用粒子群算法快速产生全局最优解信息素的初始分布,后期引入禁忌搜索算法,记录已经达到的局部最优解,在下一次搜索中,不再或者有选择地搜索这些点,从而跳出局部最优点,并且在搜索过程中允许接受劣解,充分利用禁忌搜索的记忆能力及较强的爬山能力,大大提高了获得全局最优解的概率.该算法综合了粒子群优化算法的快速性,随机性和全局收敛性以及禁忌搜索局部寻优的能力.在确保全局收敛性的基础上,能够快速搜索到高质量的优化解.该方法用于几何约束求解的性能明显高于标准粒子群算法,算法具有良好的优化性能和时间性能.

英文摘要：

The constraint problem can be transformed to an optimization problem.Particle swarm optimization（PSO） is a new evolutionary computation technique.Although PSO possesses many attractive properties,but it lacks global search ability at the end of the run.This paper introduces a hybrid approach called the TPSO that simultaneously applies particle swarm optimization（PSO）,and tabu search（TS） to create a generally well-performing search heuristics,and combat the problem of premature convergence.In the new algorithm,we consider candidate solutions and their fitness as individuals,which based on their recent search progress,the tabu search cause each particle to reset its record of its best position,to avoid making direction and velocity decisions on the basis of outdated information.The feasibility of the proposed method is demonstrated on Solving Geometric Constraint Problems.