一种求解矩形排样问题的遗传-离散粒子群优化算法

摘要

针对制造业领域的矩形优化排样问题,提出一种遗传-离散粒子群优化算法.引入交换子和交换序概念,解决了标准粒子群优化算法在求解组合优化问题时粒子的更新难以描述问题;融合遗传算法的交叉与变异思想,增强了粒子群的多样性和稳定性;同时采用改进的最低水平线搜索算法加快算法的收敛速度,并解码形成排样方案.通过实验数据对比,验证了该算法在求解矩形排样问题中的高效性和鲁棒性.%For the optimization problem of rectangular packing in manufacturing field, a genetic-discrete particle swarm optimization is proposed.Through introducing the concepts of exchange operator and exchange sequence, the difficult problem of describing the update particles in the standard particle swarm optimization algorithm for combinatorial optimization problem is solved. Combining the idea of mutation and crossover in genetic algorithm strengthens the diversity and stability of the particle swarm. An improved lowest horizontal search algorithm is proposed to accelerate the convergence speed and can be used to decode to packing layout. The experimental data show that the proposed algorithm is effective and robust in solving the problem of rectangular packing.