论文标题
对基于人群的算法的约束处理技术的综述：从单目标到多目标优化
A Review on Constraint Handling Techniques for Population-based Algorithms: from single-objective to multi-objective optimization
论文作者
论文摘要
这项介绍的研究提供了有关根据最相关的期刊，关键字，作者和文章的单一目标和多目标算法的约束处理技术的学术文献的新分析。本文回顾了基于多目标人群的优化中最先进的约束处理技术的主要思想，然后研究解决了该领域的文献计量分析。提取的论文包括研究文章，评论，书/书籍章节以及2000年至2020年之间发表的会议论文，以进行分析。结果表明，与单目标优化相比，多目标优化的约束处理技术受到了较少的关注。确定这种优化的最有希望的算法是遗传算法，差异进化算法和粒子群智能。
This presented study provides a novel analysis of scholarly literature on constraint handling techniques for single-objective and multi-objective population-based algorithms according to the most relevant journals, keywords, authors, and articles. The paper reviews the main ideas of the most state-of-the-art constraint handling techniques in multi-objective population-based optimization, and then the study addresses the bibliometric analysis in the field. The extracted papers include research articles, reviews, book/book chapters, and conference papers published between 2000 and 2020 for the analysis. The results indicate that the constraint handling techniques for multi-objective optimization have received much less attention compared with single-objective optimization. The most promising algorithms for such optimization were determined to be genetic algorithms, differential evolutionary algorithms, and particle swarm intelligence.
