基于改进型粒子群的作业车间调度问题研究

Research on job-shop scheduling problem based on improved particle swarm optimization algorithm

研究以最小化作业加工总时间为目标的作业车间调度问题,受生物体自调节机制启发,改进了传统粒子群算法,提高了搜索效率,优化了调度结果。在该研究中,借鉴生物体内部自我调节机制,在粒子群算法中引入自适应调节因子,通过单个粒子感知共同飞行的其他粒子信息来自我调整飞行状态,以达到更大的搜索范围和更好的搜索质量。最后通过对作业车间调度实例Lawerence's系列问题进行测试,测试结果验证了算法的有效性。

Taking a bio - inspired improved particle swarm optimization algorithm to minimize the maximal total processing time of the job - shop scheduling, it studies the biological adaptive modulation mechanism, proposes the adaptive regular factor to modify the updating equations of particle swarm based on the information of the par- ticles around the single particle. It improves the flying function of the particle swarm, obtains better searching ef- ficiency and searching quality. The simulation results based on benchmarks demonstrate its feasibility and effec- tiveness.