جستجوگر واژه هاي مصوب فرهنگستان زبان و ادب فارسي و واژه هاي معادل و رايج آنها در سيستم های بازيابي اطلاعات

•   آرشیو : نسخه زمستان 1397
•   موضوع : هوش مصنوعی
• نویسنده/گان : شاپوررضا برنجیان، محمد باقر دستغیب
•   کلید واژه : بهینه¬سازی، الگوریتم ژنتیک، محیط¬ پویا، حافظه، خوشه¬بندی، تخمین تراکم، جفت¬گزینی، جهش، خودسازمانده
•   Title : Optimizing dynamic issues with critical self-organization and explicit memory
•   Abstract :
  Since dynamic components, along with nonlinear constraints and multiple goals, are one of the features that appear frequently in real-world issues, and because it takes a long time for evolutionary calculations to enter the realm of industrial applications. (Especially because of their ability to deal with multifaceted and nonlinear environments) is expected to grow in the scientific community as soon as possible. Today, the use of evolutionary algorithms The basis of natural biological behaviors is of particular importance in solving these problems. Genetic algorithms can be mentioned among these algorithms. The purpose of this paper and its main claim is to enable the design of nature-inspired protocols in genetic algorithms that are effective in optimizing dynamic environments, while maintaining the complexity of the algorithm and changing the problem space. Periodic occurrences occur. In this paper, a memory-enhanced (self-organized critical) genetic algorithm is proposed to solve dynamic optimization problems. In the proposed algorithm, a new self-organizing mutation operator based on the sand dune model is used. The self-organizing mutation operator is a new mutation operator that can perform self-regulating mutation rates with a special distribution based on the sand dune model, which is suitable for dynamic optimization. If the changes occur periodically, the use of past information will normally allow the algorithm to adapt quickly to new environmental conditions as soon as the environment changes. The idea is to use a memory. One of the major challenges in using memory is diversity. To increase the level of diversity, a comparative spawning memory with Gaussian clustering was used. A solution has also been used to replace and restore memory. The proposed proposal first combines a new critical self-organized mutation with other genetic algorithms provided by other researchers, and the results show that this method has been able to repeatedly improve other genetic algorithms for dynamic environments. Finally, the proposed method of this paper, which is a combination of new critical self-organization with memory density, is presented. The results of this method will be compared with other similar methods that have been improved with the new self-organizing mutation solution. The results have been tested on various benchmarking problems as Royal Road, One Max and Deceptive functions. It has been shown that the proposed method has produced even better results than even competing methods that have been improved by the new self-organizing mutation method. The evaluation criterion in this paper is the criterion of offline efficiency and diversity criterion. The results of the experiments show that the proposed method is effective because it is compared with other methods in parameters of dynamic environments that have the best performance. As will be shown, the proposed method does not increase the set of parameters compared to other algorithms, so it meets the requirements for real-world applications.
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