摘要
针对粒子群优化算法易陷入局部最优以及求解生产调度问题时容易重复搜索的情况,结合混合车间调度问题的优化模型,提出一种改进的粒子群优化算法。在算法设计中,引入基于位置相似度的禁忌策略,避免对刚刚搜索过的区域重复搜索和过早陷入局部最优;同时采用线性微分递减方式更新惯性权重,既保证了算法前期有较高的全局搜索能力,又能保证后期有较高的开发能力。最后通过仿真实验,验证算法的有效性。
The problems will be solved in those situations when the PSO is easily converged in local optimal and repeated search in solving production scheduling problems.Combined with hybrid flow-shop scheduling optimization model,an improved particle swarm optimization algorithm is constructed.In the algorithm,the taboo strategy of the location-based similarity degree is imported to avoid repeatedly searching area which is searched before and premature local optimum search.Meanwhile,another linear differential decrement updating inertia weight strategy is also used to ensure that the algorithm has not only a higher capability in the calculation of the global search,but also to ensure the later development of higher capacity in accelerating convergence rate.Finally,the simulation experiment proves that the algorithm is effective.
出处
《计算机工程与应用》
CSCD
北大核心
2011年第31期212-214,219,共4页
Computer Engineering and Applications
关键词
混合流水车间调度
粒子群算法
禁忌策略
惯性权重
hybrid flow-shop scheduling
particle swarm optimization
taboo strategy
inertia weight
