论文标题
分支和基于NSGAII算法的绑定，用于多目标混合整数非线性优化问题
A Branch and Bound Based on NSGAII Algorithm for Multi-Objective Mixed Integer Non Linear Optimization Problems
论文作者
论文摘要
多目标混合企业非线性编程问题（MO-MINLP）出现在几个现实世界中，尤其是在机械工程领域。为了确定此类问题的良好近似帕累托前沿，我们提出了一种基于多标准分支和结合（MCBB）和非主导分类遗传算法2（NSGAII）的一般混合方法。我们提出了一项基于统计评估的计算实验，以比较拟议算法（BNB-NSGAII）与NSGAII的性能，并使用文献中知名的指标进行了比较。我们提出了一个新的指标，投资比率（IR），将解决方案的质量与消费量相关联。我们认为在本实验中，将五个现实世界的机械工程问题和两个数学问题用作测试问题。实验结果表明，BNB-NSGAII可能是解决Mo-Minlps的竞争选择。
Multi-Objective Mixed-Integer Non-Linear Programming problems (MO-MINLPs) appear in several real-world applications, especially in the mechanical engineering field. To determine a good approximated Pareto front for this type of problems, we propose a general hybrid approach based on a Multi-Criteria Branch-and-Bound (MCBB) and Non-dominated Sorting Genetic Algorithm 2 (NSGAII). We present a computational experiment based on a statistical assessment to compare the performance of the proposed algorithm (BnB-NSGAII) with NSGAII using well-known metrics from literature. We propose a new metric, Investment Ratio (IR), that relate the quality of the solution to the consumed effort. We consider five real-world mechanical engineering problems and two mathematical ones to be used as test problems in this experiment. Experimental results indicate that BnB-NSGAII could be a competitive alternative for solving MO-MINLPs.
