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Binary ant colony optimization algorithm in learning random satisfiability logic for discrete hopfield neural network
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DOI：10.1016/j.asoc.2024.112192.png)
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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.
Keyword:
Ant colony optimization
Estimation of distribution algorithm
Discrete Hopfield neural network
Metaheuristic algorithms
Classification
期刊
IF:
6.6
论文数:
1.4W
被引数:
4.8W
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A modified reverse-based analysis logic mining model with Weighted Random 2 Satisfiability logic in Discrete Hopfield Neural Network and multi-objective training of Modified Niched Genetic Algorithm离散Hopfield神经网络中带加权随机2可满足逻辑的改进逆向分析逻辑挖掘模型和改进小空间遗传算法的多目标训练
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