This paper addresses Very large‐scale integration (VLSI) placement optimization, which is important because of the rapid development of VLSI design technologies. The goal of this study is to develop a hybrid algorithm for VLSI placement. The proposed algorithm includes a sequential combination of a genetic algorithm and an evolutionary algorithm. It is commonly known that local search algorithms, such as random forest, hill climbing, and variable neighborhoods, can be effectively applied to NP‐hard problem‐solving. They provide improved solutions, which are obtained after a global search. The scientific novelty of this research is based on the development of systems, principles, and methods for creating a hybrid (combined) placement algorithm. The principal difference in the proposed algorithm is that it obtains a set of alternative solutions in parallel and then selects the best one. Nonstandard genetic operators, based on problem knowledge, are used in the proposed algorithm. An investigational study shows an objective‐function improvement of 13%. The time complexity of the hybrid placement algorithm is O(N²).

中文翻译：

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混合遗传配对置换算法可改善VLSI的放置

本文针对超大规模集成（VLSI）布局优化，由于VLSI设计技术的快速发展，这一点非常重要。这项研究的目的是开发一种用于VLSI放置的混合算法。所提出的算法包括遗传算法和进化算法的顺序组合。众所周知，本地搜索算法（例如随机森林，爬山和可变邻域）可以有效地应用于NP难题的解决。他们提供了改进的解决方案，这些解决方案是在全局搜索后获得的。这项研究的科学新颖性基于用于创建混合（组合）放置算法的系统，原理和方法的发展。所提出算法的主要区别在于，它并行获得了一组替代解决方案，然后选择了最佳解决方案。该算法使用了基于问题知识的非标准遗传算子。一项研究表明目标功能改善了13％。混合放置算法的时间复杂度为O（N ²）。