协同混合粒子群算法求解车间作业调度问题

Cooperative hybrid particle swarm optimization algorithm for job-shop scheduling problems

针对如何有效解决车间作业优化调度问题,提出一种协同粒子群和引力搜索的混合算法。新算法在粒子群算法进化停滞时引入引力搜索算法,利用引力搜索算法进化后期快速寻优的能力,及时跳出局部最优,保证全局最优。同时采用协同原理简化算法结构,提高算法收敛速度。将提出算法对车间作业调度典型测试用例进行仿真,仿真结果表明该算法较PSO和GA等算法在求解车间作业调度问题上更具优越性。

To solve the Job-shop Scheduling Problem（JSP）, a novel optimization algorithm, named as Cooperative Hybrid Particle Swarm Optimization（CHPSO）, which combines Particle Swarm Optimization（PSO）algorithm and Gravitational Search Algorithm（GSA）is presented in this paper. In CHPSO, GSA is embedded to jump out of local optimum timely and guarantee the global optimum when the PSO evolution process falls into premature convergence. Also, to simplify CHPSO＇s structure and improve the convergence speed, the cooperative principle is introduced. The proposed algorithm is performed for JSP typical test cases. The simulation results show the CHPSO algorithm obtains higher efficiency than PSO and GA algorithm for solving JSP.