In this paper, an improved multi-objective optimization method, based on learning automata (called IMOLA), is proposed and its performance on the design of a variety of functional circuits is investigated. The most important feature of the proposed method is to provide a suitable schedule for effective compromise between exploration and exploitation during the search process. To evaluate the capability of the proposed method on multi-objective problems, digital and analog circuits have been selected. The results show the superiority in comparison with new and common algorithms called non-dominated sorting genetic algorithm III, multi-objective multi verse optimization, adaptive multi-objective black hole algorithm, multi-objective modified inclined planes system optimization, and multi-objective grasshopper optimization algorithm. Evaluation of the results was reported in terms of power-delay-product, power-area-product, success rate, Pareto-front, multi-objective criteria, circuit variables, design constraints, runtime, and performance analysis.

中文翻译：

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改进的多目标学习自动机及其在VLSI电路设计中的应用

本文提出了一种改进的基于学习自动机的多目标优化方法（称为IMOLA），并研究了其在各种功能电路设计中的性能。所提出的方法的最重要特征是为搜索过程中的勘探与开发之间的有效折衷提供适当的时间表。为了评估所提出方法在多目标问题上的能力，已经选择了数字和模拟电路。结果表明，与非常规排序遗传算法III，多目标多诗词优化，自适应多目标黑洞算法，多目标修改斜面系统优化和多目标蚱hopper等新算法和常见算法相比，具有优越性。优化算法。