Improved Lyrebird optimization for multi microgrid sectionalizing and cost efficient scheduling of distributed generation by Karthik Nagarajan, Arul Rajagopalan, Mohit Bajaj, Valliappan Raju, Vojtech Blazek, Lukas Prokop

“…This paper introduces the Improved Lyrebird Optimization Algorithm (ILOA) for optimal sectionalizing and scheduling of multi-microgrid systems, aiming to minimize generation costs and active power losses while ensuring system reliability. …”
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A Virtual Power Plant-Integrated Proactive Voltage Regulation Framework for Urban Distribution Networks: Enhanced Termite Life Cycle Optimization Algorithm and Dynamic Coordination by Yonglin Li, Zhao Liu, Changtao Kan, Rongfei Qiao, Yue Yu, Changgang Li

“…., 5G base stations and data centers). While virtual power plants (VPPs) and network reconfiguration mitigate operational risks, extant methods inadequately model load flexibility and suffer from algorithmic stagnation in non-convex optimization. …”
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A novel interval prediction method in wind speed based on deep learning and combination prediction by XueJun Chen, Tao Han, Peng Cheng, Xuanfang Da

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Fuzzy deep learning architecture for cucumber plant disease detection and classification by Anas Bilal, Junaid Ali Khan, Abdulkareem Alzahrani, Khalid Almohammadi, Maha Alamri, Xiaowen Liu

“…At the same time, the ReLU transfer function ensures robustness, mainly when dealing with noisy or incomplete image segments. Feature vector optimization is performed using a chaotic particle swarm algorithm, enhancing the model’s overall accuracy, reliability, and ease of implementation. …”
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Chaos-Enhanced Fractional-Order Iterative Methods for the Stable and Efficient Solution of Nonlinear Engineering Problems by Mudassir Shams, Bruno Carpentieri

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A Bi-Layer Collaborative Planning Framework for Multi-UAV Delivery Tasks in Multi-Depot Urban Logistics by Junfu Wen, Fei Wang, Yebo Su

“…An improved particle swarm optimization (PSO) algorithm is proposed by integrating chaotic initialization, linearly adjusted acceleration coefficients and maximum velocity, a stochastic disturbance-based position update mechanism, and an adaptively tuned inertia weight to enhance algorithmic performance and path generation quality. …”
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Robust zero-watermarking for color images using hybrid deep learning models and encryption by Hager A. Gharib, Noha M. M. Abdelnapi, Khalid M. Hosny

“…Traditional zero-watermarking techniques often struggle to maintain resilience against geometric and signal processing attacks while ensuring high security and imperceptibility. …”
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Observation optimization for marine target tracking by airborne and maritime unmanned platforms cooperation using adaptive enhanced dung beetle optimization by Qiuyang Dai, Faxing Lu, Haoran Shi, Junfei Xu, Jianjing Qu

“…Existing research predominantly focuses on homogeneous platform cooperative observation optimization under random error influences, while neglecting the collaborative optimization challenges of heterogeneous platforms affected by systematic errors. …”
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Optimal Design of Low Sidelobe Sparse Linear Arrays by Li Wang, Fenggan Zhang, Banghuan Hou

“…The initial value of the algorithm is generated through the chaotic Piecewise map, while the scaling factor is adjusted dynamically in line with the iterations and fitness values. …”
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Quantitative study on weak magnetic detection defects of metal structure based on IWOA-BP algorithm by FAN Meng, TONG Bo, GAO Chen, YAO Zhongyuan, ZHANG Yu, HU Bo

“…In view of the poor effect and low efficiency of BP neural network in parameter adjustment, the improved whale optimization algorithm (IWOA) based on Sine chaotic mapping was adopted to optimize the BP neural network parameter adjustment mode, giving consideration to global optimization while improving the local optimization ability, and then the optimal parameters searched by IWOA were assigned to BP neural network, improving the quality of initial network parameters.The length, width and depth of the artificial rectangular slot were quantified by inversion. …”
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Explorative Binary Gray Wolf Optimizer with Quadratic Interpolation for Feature Selection by Yijie Zhang, Yuhang Cai

“…Therefore, feature selection becomes an essential preprocessing stage, aimed at reducing the dimensionality of the dataset by selecting the most informative features while improving classification accuracy. This paper proposes a novel binary Gray Wolf Optimization algorithm to address the feature selection problem in classification tasks. …”
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COD Optimization Prediction Model Based on CAWOA-ELM in Water Ecological Environment by Lili Jiang, Liu Yang, Yang Huang, Yi Wu, Huixian Li, XiYan Shen, Meng Bi, Lin Hong, Yiting Yang, Zuping Ding, Wenjie Chen

“…In order to detect high error rate and poor convergence of the water ecological chemical oxygen demand (COD) prediction model, combining the limit learning machine (ELM) model and whale optimization algorithm, CAWOA is improved by the sin chaos search strategy, while the ELM optimizes the parameters of the algorithm to improve convergence speed, thus improving the generalization performance of the ELM. …”
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Viewpoint Selection for 3D Scenes in Map Narratives by Shichuan Liu, Yong Wang, Qing Tang, Yaoyao Han

“…Pearson’s correlation coefficient is used to evaluate the relationship between visual salience and narrative relevance, serving as a constraint to construct a viewpoint fitness function that integrates the visual salience of the convex polyhedron enclosing the scene. The chaotic particle swarm optimization (CPSO) algorithm is utilized to locate the viewpoint position while maximizing the fitness function, identifying a viewpoint meeting narrative and visual salience requirements. …”
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Aircraft range fuel prediction study based on WPD with IAPO optimized BiLSTM–KAN model by Weizhen Tang, Jie Dai, Yuantai Li

“…Additionally, the SPM chaotic mapping strategy is utilized for population initialization, while the introduction of the golden sine operator variation strategy enhances the local search capabilities of the algorithm. …”
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Time-windows constrained electric logistics vehicle routing optimization model and its dispatching benefits analysis in the energy market by Jiwei Wang, Jun Mo, Hui Yang

“…For the price-based demand response mechanism, the electric logistics vehicle routing with time windows optimization model is constructed, with the travel routes and distance matrix calculated using the adaptive chaotic genetic algorithm and dueling Deep Q-Network, respectively. …”
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Reinforcement Learning With Deep Features: A Dynamic Approach for Intrusion Detection in IoT Networks by Mohamad Khayat, Ezedin Barka, Mohamed Adel Serhani, Farag Sallabi, Khaled Shuaib, Heba M. Khater

“…The extracted feature sets were concatenated within a feature fusion layer that enabled efficient dimensionality reduction via principal component analysis. A hybrid optimization approach was also introduced for feature selection using green anaconda optimization and the chaotic learning osprey optimization algorithm. …”
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Stochastic sizing and energy management of a hybrid energy system using cloud model and improved Walrus optimizer for China regions by Wenjun Liao, Qing Xiong, Zilong Chen, Jinhui Tan, Pingfei Li, Hadi Gharoei

“…An improved Walrus Optimizer (IWO) with a piecewise linear chaotic map is applied to determine the optimal system component sizes. …”
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Nonlinear Hysteresis Parameter Identification of Piezoelectric Actuators Using an Improved Gray Wolf Optimizer with Logistic Chaos Initialization and a Levy Flight Variant by Yonggang Yan, Kangqiao Duan, Jianjun Cui, Shiwei Guo, Can Cui, Yongsheng Zhou, Junjie Huang, Geng Wang, Dengpan Zhang, Fumin Zhang

“…A logistic chaotic mapping method is introduced for population initialization, while a nonlinear convergence factor and a Levy flight strategy are incorporated to enhance global search capabilities during the later stages of optimization. …”
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Application of a Hybrid Model Based on CEEMDAN and IMSA in Water Quality Prediction by GUO Li-jin, WU Hao-tian

“…[Results] The study introduced four key innovations to the original Mantis Search Algorithm (MSA): (1) combining Logistic-Tent chaotic mapping for population initialization, ensuring uniform and random distribution of initial solutions to enhance global search capability and convergence speed; (2) nonlinear acceleration factor, refining MSA’s core update formula to transition from global exploration to local exploitation, mitigating local optima entrapment; (3) elite-guided adaptive update strategy, addressing the excessive randomness in the position update strategy when Mantis attacks fail, improving late-stage search efficiency while preserving some randomness; (4) opposition-based learning, generating individuals opposite to the current individual to enhance global optimization. …”
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The prediction of karst-collapse susceptibility levels based on the ISSA-ELM integrated model by Jiaxin Wang, Ying Yang, Xian Yang, Yulong Lu, Yang Liu, Da Hu, Yongjia Hu

“…This research offers a solid scientific foundation for risk classification and hazard mitigation strategies while introducing a novel methodological framework through the integration of innovative algorithms. …”
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