A Meta-Heuristic Algorithm Based on the Happiness Model

Aref Yelghı

Recent work has attempted to determine the appropriate global minimum for complex problems. The paper presents a population and direct-based swarm optimization algorithm called the happiness optimizer (HPO) algorithm. An HPO algorithm is designed based on personal behavior and demonstrated in 30 and 100 dimensions on benchmark functions. The model includes four questions: “what do you want?”, “what do you have?”, “what do others have?”, and “what happened?”, which guide the development of a happiness behavior model. By considering the balancing between exploration and exploitation operators in ...Daha fazlası

Makale 2023 Studies in Computational Intelligence 1069 , pp. 1 - 126 84 Görüntülenme

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Detection of cyber-attacks on smart grids using improved VGG19 deep neural network architecture and Aquila optimizer algorithm

Cevat Rahebi

This study introduces an innovative smart grid (SG) intrusion detection system, integrating Game Theory, swarm intelligence, and deep learning (DL) to protect against complex cyber-attacks. This method balances training samples by employing conditional DL using Game Theory and CGAN. The Aquila optimizer (AO) algorithm selects features, mapping them onto the dataset and converting them into RGB color images for training a VGG19 neural network. AO optimizes meta-parameters, enhancing VGG19 accuracy. Testing on the NSL-KDD dataset generates remarkable results: 99.82% accuracy, 99.69% sensitivity, ...Daha fazlası

Makale 2024 Signal, Image and Video Processing18 ( 2 ) , pp. 1 - 1491 19 Görüntülenme