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Neighborhood research approach in swarm intelligence for solving the optimization problems

机译：群体智能的邻域研究方法解决优化问题

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The article discusses the key problem of swarm algorithms and the bioinspired approach, which is to determine the proximity function and study the emerging neighborhoods in order to solve optimization problems. There is a detailed discussion of one of the most important design phases, namely, the VLSI components placement problem, whose solutions fineness directly affects the quality of circuit tracing. The solution of the neighborhoods and solution proximity problem is demonstrated by the study of the solutions by means of hybrid search methods. The main idea of this approach is the sequential use of genetic and swarm algorithms. We propose a new formation principle of the positions' neighborhood in the solution space based on the bee colony algorithm, which uses the concept of neighborhood in a circular search space. There are also experimental studies which show that the time complexity of the developed approach does not go beyond polynomial dependence.
机译：本文讨论了群算法和生物启发方法的关键问题，即确定邻近函数并研究新兴邻域，以解决优化问题。详细讨论了最重要的设计阶段之一，即VLSI组件放置问题，其解决方案的精细度直接影响电路跟踪的质量。通过混合搜索方法对解的研究证明了邻域解和解邻近问题。这种方法的主要思想是遗传算法和群算法的顺序使用。提出了一种基于蜂群算法的求解空间中位置邻域的新形成原理，该算法采用了圆形搜索空间中邻域的概念。也有实验研究表明，所开发方法的时间复杂度不会超出多项式依赖性。

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