Binary ant colony optimization algorithm in learning random satisfiability logic for discrete hopfield neural network

• 主要な著者: Gao, Yuan, Mohd Kasihmuddin, Mohd Shareduwan, Chen, Ju, Zheng, Chengfeng, Romli, Nurul Atiqah, Mansor, Mohd. Asyraf, Zamri, Nur Ezlin
• フォーマット: 論文
• 言語: English
• 出版事項: Elsevier 2024
• オンライン･アクセス: http://psasir.upm.edu.my/id/eprint/114433/1/114433.pdf; http://psasir.upm.edu.my/id/eprint/114433/; https://linkinghub.elsevier.com/retrieve/pii/S1568494624009669

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 embedde...