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Neighborhood research approach in swarm intelligence for solving the optimization problems

机译:群落智能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:本文讨论了群体算法的关键问题和生物透明的方法,即确定近似功能并研究新兴街区以解决优化问题。 有一个详细的讨论最重要的设计阶段之一,即VLSI组件放置问题,其解决方案细度直接影响电路跟踪的质量。 通过混合搜索方法研究解决方案来证明邻域和解决方案接近问题的解决方案。 这种方法的主要思想是遗传和群算法的连续使用。 我们提出了一种基于蜜蜂殖民地算法的解决方案空间中职位邻域的新形成原则,其在循环搜索空间中使用邻域的概念。 还有实验研究表明,发育方法的时间复杂性不会超越多项式依赖。

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