{"title":"学习离散跳场神经网络随机满足逻辑的二元蚁群优化算法","authors":"","doi":"10.1016/j.asoc.2024.112192","DOIUrl":null,"url":null,"abstract":"<div><p>This study introduced a novel ant colony optimization algorithm that implements the population selection strategy of the Estimation of Distribution Algorithm and a new pheromone updating formula. It aimed to optimize the performance of G-type random high-order satisfiability logic structures embedded in Discrete Hopfield Neural Networks, thereby enhancing the efficiency of the Hopfield Neural Network learning algorithm. Through comparative analysis with other metaheuristic algorithms, our model demonstrated superior performance in terms of global convergence, time complexity, and algorithm complexity. Additionally, we evaluated the learning phase, retrieval phase, and similarity analysis using various ratios of literals and clauses. It was shown that our proposed model exhibits stronger search ability compared to other metaheuristic algorithms and Exhaustive Search. Our model enhanced the efficiency of the learning phase, resulting in the number of global solutions accounting for 100 %, and significantly improved the global solution diversity. These advancements contributed to the efficiency of the model in convergence, rendering it applicable to a wide range of nonlinear classification and prediction problems.</p></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":null,"pages":null},"PeriodicalIF":7.2000,"publicationDate":"2024-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Binary ant colony optimization algorithm in learning random satisfiability logic for discrete hopfield neural network\",\"authors\":\"\",\"doi\":\"10.1016/j.asoc.2024.112192\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>This study introduced a novel ant colony optimization algorithm that implements the population selection strategy of the Estimation of Distribution Algorithm and a new pheromone updating formula. It aimed to optimize the performance of G-type random high-order satisfiability logic structures embedded in Discrete Hopfield Neural Networks, thereby enhancing the efficiency of the Hopfield Neural Network learning algorithm. Through comparative analysis with other metaheuristic algorithms, our model demonstrated superior performance in terms of global convergence, time complexity, and algorithm complexity. Additionally, we evaluated the learning phase, retrieval phase, and similarity analysis using various ratios of literals and clauses. It was shown that our proposed model exhibits stronger search ability compared to other metaheuristic algorithms and Exhaustive Search. Our model enhanced the efficiency of the learning phase, resulting in the number of global solutions accounting for 100 %, and significantly improved the global solution diversity. These advancements contributed to the efficiency of the model in convergence, rendering it applicable to a wide range of nonlinear classification and prediction problems.</p></div>\",\"PeriodicalId\":50737,\"journal\":{\"name\":\"Applied Soft Computing\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":7.2000,\"publicationDate\":\"2024-09-12\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Applied Soft Computing\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S1568494624009669\",\"RegionNum\":1,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Applied Soft Computing","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1568494624009669","RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 0
摘要
本研究介绍了一种新型蚁群优化算法,该算法实现了分布估计算法的种群选择策略和新的信息素更新公式。该算法旨在优化嵌入离散 Hopfield 神经网络的 G 型随机高阶可满足性逻辑结构的性能,从而提高 Hopfield 神经网络学习算法的效率。通过与其他元启发式算法的比较分析,我们的模型在全局收敛性、时间复杂性和算法复杂性方面都表现出了卓越的性能。此外,我们还评估了学习阶段、检索阶段以及使用各种字面和分句比例进行的相似性分析。结果表明,与其他元启发式算法和穷举搜索相比,我们提出的模型具有更强的搜索能力。我们的模型提高了学习阶段的效率,使全局解的数量占到 100%,并显著改善了全局解的多样性。这些进步提高了模型的收敛效率,使其适用于广泛的非线性分类和预测问题。
Binary ant colony optimization algorithm in learning random satisfiability logic for discrete hopfield neural network
This study introduced a novel ant colony optimization algorithm that implements the population selection strategy of the Estimation of Distribution Algorithm and a new pheromone updating formula. It aimed to optimize the performance of G-type random high-order satisfiability logic structures embedded in Discrete Hopfield Neural Networks, thereby enhancing the efficiency of the Hopfield Neural Network learning algorithm. Through comparative analysis with other metaheuristic algorithms, our model demonstrated superior performance in terms of global convergence, time complexity, and algorithm complexity. Additionally, we evaluated the learning phase, retrieval phase, and similarity analysis using various ratios of literals and clauses. It was shown that our proposed model exhibits stronger search ability compared to other metaheuristic algorithms and Exhaustive Search. Our model enhanced the efficiency of the learning phase, resulting in the number of global solutions accounting for 100 %, and significantly improved the global solution diversity. These advancements contributed to the efficiency of the model in convergence, rendering it applicable to a wide range of nonlinear classification and prediction problems.
期刊介绍:
Applied Soft Computing is an international journal promoting an integrated view of soft computing to solve real life problems.The focus is to publish the highest quality research in application and convergence of the areas of Fuzzy Logic, Neural Networks, Evolutionary Computing, Rough Sets and other similar techniques to address real world complexities.
Applied Soft Computing is a rolling publication: articles are published as soon as the editor-in-chief has accepted them. Therefore, the web site will continuously be updated with new articles and the publication time will be short.