Multi-Peak Photovoltaic Maximum Power Point Tracking Method Based on Honey Badger Algorithm Under Localized Shading Conditions by Qianjin Gui, Lei Wang, Chao Ding, Wenfa Xu, Xiaoyang Li, Feilong Yu, Haisen Wang

“…The performance of this method is also compared and analyzed with the traditional MPPT methods based on the perturbation observation (P&O) method and Particle Swarm Optimization (PSO) algorithm. The experimental results have proven that, compared with the MPPT methods based on P&O and PSO, the proposed multi-peak MPPT method based on the HBA algorithm has a faster tracking speed, higher tracking accuracy, and fewer iterations.…”
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Effective Modeling of Photovoltaic Modules Using Sailfish Optimizer by Mohammed Bilal Danoune, A. Djafour, Youcef Rehouma, A. Degla, Zied Dress

“…Moreover, to show the efficacy of the algorithm, the results are compared with some literature techniques such as Salp-Swarm-Optimizer (SSA), Whale Optimization (WOA), Artificial-Bee-Colony (ABC), and Particle-Swarm Optimization (PSO) methods. …”
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Hybrid optimized data aggregation for fog computing devices in internet of things by M. Jalasri, S. Manikandan, Arthur Davis Nicholas, S. Gobimohan, Naarisetti Srinivasa Rao

“…In this work, a new and novel hybrid optimization technique based on TABU Search (TS), Particle Swarm Optimization (PSO), and River Formation Dynamics (RFD) algorithms were proposed. …”
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Adaptive Ruminant Optimization With LoRa-Based Communication for Formation Control of Multiple UAVs by Muhammad Aamir Khan, Zain Anwar Ali, Muhammad Haris Muneer, Raza Hasan

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Multi-objective optimization and parameter sensitivity study on microreactor nuclear power systems by Ersheng You, Yiyi Li, Jianjun Xu, Dianchuan Xing, Haochun Zhang

“…Aiming at the MRNPS based on Brayton cycles, the particle swarm optimization (PSO) method was used to carry out the optimization calculation and analysis of three target parameters: system thermal efficiency, power-to-weight ratio and radiator heat removal area. …”
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Comparative Evaluation of Reinforcement Learning Algorithms for Multi-Agent Unmanned Aerial Vehicle Path Planning in 2D and 3D Environments by Mirza Aqib Ali, Adnan Maqsood, Usama Athar, Hasan Raza Khanzada

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Multi-objective optimization framework for electric vehicle charging and discharging scheduling in distribution networks using the red deer algorithm by Sahbi Boubaker, Habib Kraiem, Nejib Ghazouani, Souad Kamel, Adel Mellit, Faisal S. Alsubaei, Farid Bourennani, Walid Meskine, Tariq Alqubaysi

“…To tackle the optimization problem, a metaheuristic swarm intelligence algorithm, the Red Deer Algorithm (RDA), is utilized to determine the optimal EV charging and discharging timings. …”
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Enhancing automated detection and classification of dementia in individuals with cognitive impairment using artificial intelligence techniques by Shoayee Dlaim Alotaibi, Abeer A. K. Alharbi

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A Thinning Method of Linear And Planar Array Antennas To Reduce SLL of Radiation Pattern By GWO And ICA Algorithms by H. Rezagholizadeh, D. Gharavian

“…Thinning is performed using Genetic, Particle Swarm, Imperialist Competitive and Grey Wolf algorithms. …”
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MULTI-MODEL STACK ENSEMBLE DEEP LEARNING APPROACH FOR MULTI-DISEASE PREDICTION IN HEALTHCARE APPLICATION by Bhaskar Adepu, T. Archana

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A refined Greylag Goose optimization method for effective IoT service allocation in edge computing systems by Hossein Najafi Khosrowshahi, Hadi S. Aghdasi, Pedram Salehpour

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M-Race: A Racing Algorithm for the Tuning of Meta-Heuristics Based on Multiple Performance Objectives by Christoff Jordaan, Andries Engelbrecht, Kian Anderson

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A Review of QoS-Driven Task Scheduling Algorithms and Their Impact on Data Quality in Process Management by Anupam Yadav, Ashish Sharma

“…According to this study, the scheduling algorithms that are used by researchers 80% of the time include the genetic algorithm in bio-inspired systems and particle swarm optimization in swarm intelligence. …”
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Using Optimization Algorithms for Effective Missing-Data Imputation: A Case Study of Tabular Data Derived from Video Surveillance by Mahmoud M. Eid, Kamal ElDahshan, Abdelatif H. Abouali, Alaa Tharwat

“…The first set uses synthetic datasets to compare four optimization algorithms—Particle Swarm Optimization (PSO), Grey Wolf Optimization (GWO), Whale Optimization Algorithm (WOA), and the Sine–Cosine Algorithm (SCA)—to determine which one best identifies features related to the target feature. …”
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A Comparative Analysis of Swarm Intelligence Techniques for Feature Selection in Cancer Classification by Chellamuthu Gunavathi, Kandasamy Premalatha

“…In SFLLF, the Lévy flight is included to avoid premature convergence of shuffled frog leaping (SFL) algorithm. The SI techniques such as particle swarm optimization (PSO), cuckoo search (CS), SFL, and SFLLF are used for feature selection which identifies informative genes for classification. …”
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An Intrusion Detection System Based on Deep Learning and Metaheuristic Algorithm for IOT by Bahman Sanjabi, Mahmood Ahmadi

“…In this paper, a method is presented that uses meta-heuristic algorithms such as genetic algorithm, particle swarm optimization, artificial bee colony and gray wolf to find the optimal hyperparameters for the deep learning network and the intrusion detection system is created based on these hyperparameters. …”
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A metaheuristic optimization framework inspired by virus mutations and its ability to optimize the structural design of 2D and 3D steel frames compared to other methods by Mehdi Ghasri, Hamid Reza Karimi, Abdolhamid Salarnia

“…MVPO integrates population-based exploration, simulating viral spread through intra-community interactions (guided by wavelet functions to model transmission variability) and inter-community propagation, with a two-phase mutation strategy: the first phase enhances global exploration via elongated search steps during accelerated spread, while the second phase introduces structural perturbations to escape local optima. The algorithm’s efficacy is evaluated through three benchmark problems, two planar (2D) frames and a three-dimensional (3D) space frame, with comparisons against established methods (e.g., particle swarm optimization, cuckoo search) and contemporary algorithms (e.g., marine predators algorithm, pelican optimization algorithm). …”
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Research on Optimal Dispatching of Multi-Microgrid Considering Economy by Li HE, Hongfang LÜ

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Multi-objective coordinated control and optimization for photovoltaic microgrid scheduling by Da Yu, Kai Hou, Xu Lin, Guoyang Cai, Xin Shan, Weihua Wang

“…This paper proposes a multi-objective coordinated control and optimization system for PV microgrids. To address the challenges of slow convergence and local optima in traditional PV microgrid scheduling methods, this study introduced an improved multiple objective particle swarm optimization (IMOPSO) algorithm that integrates an adaptive inertia weight adjustment strategy based on optimal similarity and a multi-directional iterative Pareto solution archive update mechanism. …”
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AUV path planning method based on improved sparrow search algorithm by Lijun TANG, Yunxia FAN, Xingyu ZHOU, Qian SUN

“…Results Simulation results demonstrate that the ISSA significantly outperforms the original SSA and other state-of-the-art algorithms, such as particle swarm optimization (PSO), differential evolution (DE), artificial bee colony (ABC), and whale optimization algorithm (WOA). …”
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