One of the main objectives of computer-aided process planning is to determine the optimum machining sequences and setups. Among the different methods to implement this task, it can be named the constrained optimization algorithms. The immediate drawback of these algorithms is usually a large space needed to be searched for the solution. This can easily hinder the convergence of the solution and increase the possibility of getting trapped in the local minima. A novel approach has been developed in this work with the objective of reducing the search space. It is based on consolidating the decisive factors influencing the consecutive features. This helps prevent creation of sequences which need the change of setup, machine tool, and cutting tool. The proposed method has been applied to three different optimization methods, including genetic, particle swarm, and simulated annealing algorithms. It is shown that these algorithms with reduced search spaces outperform those reported in the literature, with respect to the convergence rate. The best results are found in the genetic algorithm from the viewpoint of the obtained solution and the convergence rate. The worst results belong to the particle swarm algorithm in connection with the strategy of generating new solutions.

中文翻译：

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通过合并决定性代理减少用于确定加工顺序的优化算法的搜索空间

计算机辅助工艺规划的主要目标之一是确定最佳加工顺序和设置。在实现此任务的不同方法中，可以将其命名为约束优化算法。这些算法的直接缺点通常是需要搜索大量空间才能找到解决方案。这很容易阻碍解的收敛并增加陷入局部最小值的可能性。在这项工作中开发了一种新方法，目的是减少搜索空间。它基于巩固影响连续特征的决定性因素。这有助于防止创建需要更改设置、机床和切削刀具的序列。所提出的方法已应用于三种不同的优化方法，包括遗传、粒子群算法和模拟退火算法。结果表明，这些具有减少搜索空间的算法在收敛速度方面优于文献中报道的算法。从得到的解和收敛速度的角度来看，在遗传算法中找到了最好的结果。最差的结果属于与生成新解的策略相关的粒子群算法。