矩形件排样优化问题的DPSO-CG混合优化算法研究

Research on DPSO-CG Hybrid Optimization Algorithm for Layout OptimizationProblem of Rectangular Parts

针对矩形件排样问题非线性、高复杂计算性等特点,提出一种结合离散粒子群优化(DPSO)算法和混沌遗传(CG)策略的DPSO-CG混合优化算法。利用混沌运动的遍历性和随机性,引入混沌交叉和混沌变异的遗传操作,通过增加个体的多样性,增强算法全局搜索能力,结合最低水平线定位算法,实现矩形件排样后板材利用率的提高。最后针对实例进行排样优化验证,排样结果表明,DPSO-CG混合优化算法能够使板材利用率最大值达到0.9417,实现更优的排样,验证了算法的正确性和有效性。

In view of the nonlinearity and high complexity of the rectangular partslayout problem,a DPSO-CG hybrid optimization algorithm combining discrete particle swarm optimization(DPSO)algorithm and chaotic genetics(CG)strategy was proposed.The algorithm uses the ergodic and random nature of chaotic motion,the genetic operation of chaotic crossover and chaotic mutation is introduced,By increasing the diversity of individuals,enhancing the global search ability of the algorithm,and combining with the lowest horizontal line positioning algorithm,the utilization rate of the plate after rectangular parts layout is improved.Finally,the layout optimization verification is carried out for examples,and the layout results show that the DPSO-CG hybrid optimization algorithm can make the maximum utilization rate of the plate reach 0.9417,achieving better layout,and verifying the correctness and effectiveness of the algorithm.