Department of Mechanical Engineering, Hubei University of Automotive Technology, Shiyan, China.
PLoS One. 2020 Oct 9;15(10):e0240131. doi: 10.1371/journal.pone.0240131. eCollection 2020.
In the EMO (evolutionary multi-objective, EMO) algorithm, MaOPs (many objective optimization problems, MaOPs) are sometimes difficult to keep the balance of convergence and diversity. The decomposition based EMO developed for MaOPs has been proved to be effective, and BBO/Complex (the biogeography based optimization for complex system, BBO/Complex) algorithm is a low complexity algorithm. In this paper, a decomposition and adaptive weight adjustment based BBO/Complex algorithm (DAWA-BBO/Complex) for MaOPs is proposed. First, a new method based on crowding distance is designed to generate a set of weight vectors with good uniformly. Second, an adaptive weight adjustment method is used to solve MaOPs with complex Pareto optimal front. Subsystem space obtains a non-dominated solution by a new selection strategy. The experimental results show that the algorithm is superior to other new algorithms in terms of convergence and diversity in DTLZ benchmark problems. Finally, the algorithm is used to solve the problem of NC (numerical control machine, NC) cutting parameters, and the final optimization result is obtained by AHP (Analytic Hierarchy Process, AHP) method. The results show that the cutting speed is 10.8m/min, back cutting depth is 0.13mm, the cutting time is 504s and the cutting cost is 22.15yuan. The proposed algorithm can effectively solve the practical optimization problem.
在 EMO（进化多目标，EMO）算法中，MaOPs（多目标优化问题，MaOPs）有时难以保持收敛性和多样性的平衡。针对 MaOPs 开发的基于分解的 EMO 已被证明是有效的，而 BBO/Complex（用于复杂系统的生物地理学优化，BBO/Complex）算法是一种低复杂度算法。本文提出了一种基于分解和自适应权重调整的 BBO/Complex 算法（DAWA-BBO/Complex）用于 MaOPs。首先，设计了一种基于拥挤距离的新方法来生成一组具有良好均匀性的权重向量。其次，使用自适应权重调整方法来解决具有复杂 Pareto 最优前沿的 MaOPs。子系统空间通过新的选择策略获得非支配解。实验结果表明，该算法在 DTLZ 基准问题上在收敛性和多样性方面优于其他新算法。最后，该算法用于解决数控机床（NC）切削参数问题，并通过层次分析法（AHP）方法获得最终优化结果。结果表明，切削速度为 10.8m/min，背切深度为 0.13mm，切削时间为 504s，切削成本为 22.15 元。所提出的算法可以有效地解决实际优化问题。
