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    A review of mothflame optimization algorithm: analysis and applications
    (Elsevier, 2024) Abualigah, Laith; Al-Abadi, Laheeb; Ikotun, Abiodun M.; AL-Saqqar, Faisal; Izci, Davut; Zhang, Peiying; Sumari, Putra
    The moth flame optimization (MFO) algorithm, which is inspired by nature, is studied in this study. This optimizer takes its main inspiration from the directional navigation approach used by moths in nature. Moths fly at a steady angle to the moon at night. This is a particularly efficient method for traveling long distances in a straight line. Insects, on the other hand, are trapped in a useless/deadly spiral path around artificial lights. This chapter provides a review of published papers that used the MFO method. © 2024 Elsevier Inc. All rights reserved.
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    Deep Reinforcement Learning: Bridging Learning and Control in Intelligent Systems
    (CRC Press, 2025) Izci, Davut; Thanh, Hung Vo; Zitar, Raed Abu; Al-Okbi, Nada Khalil; Liu, Zhe; Şahin, Canan Batur; Abualigah, Laith
    Deep Reinforcement Learning (DRL) has gained popularity as a new approach in artificial intelligence that successfully integrates representation learning through Deep Learning with decision making in Reinforcement Learning. In this work, we investigate basic concepts of DRL design, structural composition, and scope of usage in intelligent systems. More specifically, several benchmark tasks were assigned to test states and actions of DRL algorithms such as Deep Q-Networks (DQN) and Proximal Policy Optimization (PPO). Empirical evaluations illustrate the potential of DRL in dealing with complex tasks. Sample efficiency, stability and interpretability issues of DRL are also reviewed in the paper. Suggestions for further work are concentrated on algorithm increase of robustness, training time and expense decrease as well as enhancement of implementation efficiency in practical environment. © 2025 Laith Abualigah.