An Enhanced PSO-Based Clustering Energy Optimization Algorithm for Wireless Sensor Network by C. Vimalarani, R. Subramanian, S. N. Sivanandam

“…This paper proposes an Enhanced PSO-Based Clustering Energy Optimization (EPSO-CEO) algorithm for Wireless Sensor Network in which clustering and clustering head selection are done by using Particle Swarm Optimization (PSO) algorithm with respect to minimizing the power consumption in WSN. …”
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Prediction of Shear Strength of Steel Fiber-Reinforced Concrete Beams with Stirrups Using Hybrid Machine Learning and Deep Learning Models by B. R. Kavya, A. S. Shrikanth, K. S. Sreekeshava

“…In the present research effort, a hybrid support vector regression model combined with a particle swarm optimization algorithm is provided, to explore the relationship between the material and dimensional characteristics of a concrete beam and its shear strength. …”
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Constraint Consensus Based Artificial Bee Colony Algorithm for Constrained Optimization Problems by Liling Sun, Yuhan Wu, Xiaodan Liang, Maowei He, Hanning Chen

“…The performance of the proposed ABCCC is verified by a set of constrained benchmark problems comparing with two state-of-the-art CC-based EAs, including particle swarm optimization based on CC (PSOCC) and differential evolution based on CC (DECC). …”
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Pattern Nulling of Linear Antenna Arrays Using Backtracking Search Optimization Algorithm by Kerim Guney, Ali Durmus

“…The results obtained by BSA are compared with the results of the following seventeen algorithms: particle swarm optimization (PSO), genetic algorithm (GA), modified touring ant colony algorithm (MTACO), quadratic programming method (QPM), bacterial foraging algorithm (BFA), bees algorithm (BA), clonal selection algorithm (CLONALG), plant growth simulation algorithm (PGSA), tabu search algorithm (TSA), memetic algorithm (MA), nondominated sorting GA-2 (NSGA-2), multiobjective differential evolution (MODE), decomposition with differential evolution (MOEA/D-DE), comprehensive learning PSO (CLPSO), harmony search algorithm (HSA), seeker optimization algorithm (SOA), and mean variance mapping optimization (MVMO). …”
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Optimization Planning Techniques with Meta-Heuristic Algorithms in IoT: Performance and QoS Evaluation by İsa Avcı, Murat Koca

“…These algorithms include the particle swarm algorithm, the genetic algorithm, and the ant colony algorithm. …”
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Design and Control for Piezoelectric Energy Harvester Based on a Heuristic Optimization Algorithms by Habeeb Jaber Nekad, Diyah Kammel Shary, Oday Alahmad

“…The parameters of the second controller will be optimized by using various heuristic algorithms. (Ant Colony Optimization (ACO), Modified Camel Traveling Algorithm (MCTA), and Particle Swarm Optimization (PSO)). …”
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Optimal Energy Management of Microgrids using Quantum Teaching Learning Based Algorithm by Zahra Esmaeili, Seyed Hamid Hosseini

“…First, by maximizing the operation cost of the microgrid, the worst case for the uncertain parameters is determined using Particle Swarm Optimization (PSO). Then, according to the results obtained in the first level, by minimizing the operation cost of the microgrid, the final optimal solution is obtained using the Quantum TLBO (QTLBO). …”
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Applied Artificial Bee Colony Optimization Algorithm in Fire Evacuation Routing System by Chen Wang, Lincoln C. Wood, Heng Li, Zhenye Aw, Abolfazl Keshavarzsaleh

“…However, the existing fire evacuation routing models using various algorithm such as ant colony optimization or particle swarm optimization can neither properly interpret the delay caused by congestion during evacuation nor determine the best layout of emergency exit guidance signs; thus bee colony optimization is expected to solve the problem. …”
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Evaluating the Nuclear Reaction Optimization (NRO) Algorithm for Gene Selection in Cancer Classification by Shahad Alkamli, Hala Alshamlan

“…Comparative analysis with Harris Hawks Optimization (HHO), Artificial Bee Colony (ABC), Particle Swarm Optimization (PSO), and Firefly Algorithm (FFA) shows that while NRO delivers competitive performance, it does not consistently outperform all methods across datasets. …”
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Maximum likelihood DOA estimation based on improved ant colony optimization algorithm by JIAO Yameng, LI Wenping, WU Yue, CUI Lin

“…Simulation results show that, compared with ML estimation methods based on particle swarm optimization (PSO) algorithm and ACO algorithm, the convergence speed of the ML-MACO algorithm is 4 times faster than that of the ML-ACO algorithm, the computational load is 1/3 that of the ML-ACO algorithm, the resolution success rate is higher than the ML-PSO algorithm and the ML-ACO algorithm, and the estimation error is less than the ML-PSO algorithm and the ML-ACO algorithm. …”
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Queueing modeling and optimization of hybrid electric vehicle infrastructures using evolutionary algorithms by Shreekant Varshney, Manthan Shah, Bhasuru Abhinaya Srinivas, Mayank Gupta, Kaibalya Prasad Panda

“…The novelty of the proposed research is to formulate the cost optimization problem and demonstrate optimal converging outcomes through an in-depth comparative investigation of multiple evolutionary algorithms, including the particle swarm optimization (PSO), cuckoo search (CS), grey wolf (GW), and honey badger (HB) optimizers. …”
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Commercial Vehicle Ride Comfort Optimization Based on Intelligent Algorithms and Nonlinear Damping by Shuilong He, Keren Chen, Enyong Xu, Wei Wang, Zhansi Jiang

“…The sum of the frequency-weighted RMS of the driver seat acceleration, the RMS of dynamic tyre load, and suspension working space is taken as the objective function in this article, using nonlinear damping coefficients and stiffness of suspension as design variables. By applying the particle swarm optimization (PSO), cuckoo search (CS), dividing rectangles (DIRECT), and genetic algorithm (GA), a set of optimal solutions are obtained. …”
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Optimizing Photovoltaic Panel Performance: A Comparative Study of Meta-Heuristic Algorithms by M. Sundar Rajan

“…This paper addresses the parameter estimation of four distinct PV panel models—PV-RTC, PV-PWP 201, PV-STM6 40/36, and PV-STP6 120/36—using a range of meta-heuristic optimization algorithms. The centerpiece of this study is the introduction of a novel hybrid algorithm, HPSGWO, which combines Particle Swarm Optimization (PSO) and Grey Wolf Optimizer (GWO) techniques. …”
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Evaluation of the Efficiency of Metaheuristic Algorithms in the Optimal Design of Pile Wall Retaining Systems by Farshad Taiyari

“…Four different metaheuristic algorithms, namely Genetic, Particle swarm optimization, Bee, and Biogeography-based algorithms, are chosen for the optimization problem. …”
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Predicting the Classification of Heart Failure Patients Using Optimized Machine Learning Algorithms by Marzia Ahmed, Mohd Herwan Sulaiman, Md Maruf Hassan, Touhid Bhuiyan

“…This study proposes an optimized machine learning approach using Gradient Boosting Machine (GBM) and Adaptive Inertia Weight Particle Swarm Optimization (AIW-PSO) to predict heart failure survival. …”
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Comparative Analysis of Optimization Algorithms to Enhance WPGSs Performance: Malaysia Case Study by Hussein Shutari, Taib Ibrahim, Nursyarizal Bin Mohd Nor, Hakim Abdulrab, Yahya Z. Alharthi, Mohammad Faridun Naim Tajuddin, Maveeya Baba

“…The growing interest in wind power and its expanding integration into the electrical power grid necessitate significant efforts to enhance the efficiency and performance of wind power generation Systems (WPGS) through optimized design and operation. This paper investigates the implementation of five optimization algorithms:sine cosine algorithm (SCA), grey wolf optimizer (GWO), particle swarm optimizer (PSO), transient search optimization (TSO), and a hybrid sine cosine algorithm-transient search optimizer (HSCATSO), to optimally design control schemes (ODCSs) of frequency converters within grid-connected WPGS. …”
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Black Hole Algorithm for Software Requirements Prioritization by Norah Ibrahim Alfassam, M. Abdullah-Al-Wadud, Mubarak Rashed Alrashoud

“…Furthermore, the proposed BHA-based solution was evaluated on three real-world datasets (RALIC, Word, and ReleasePlanner), and its performance was compared with that of multiple state-of-the-art algorithms, including Ant Colony Optimization (ACO), Genetic Algorithm (GA), Grey Wolf Optimizer (GWO), Particle Swarm Optimization (PSO), Fitness Dependent Optimizer (FDO), Goose Algorithm (GAO), and Lagrange Elementary Optimization (LEO). …”
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Sustainable closed-loop supply chain network planning considering price competition using particle chaotic ant colony algorithm by Tianrui Zhang, Quanfeng He, Weibo Zhao, Mingqi Wei

“…Thirdly, the problems of premature convergence and slow convergence during the traditional particle swarm optimization algorithm and genetic algorithm are solved with the particle chaotic ant colony algorithm (PSCACO). …”
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Research on Capacitated Multi-Ship Replenishment Path Planning Problem Based on the Synergistic Hybrid Optimization Algorithm by Lin Yang, Qinghua Chen, Junjie Mu, Tangying Liu, Xiaoxiao Li, Shuxiang Cai

“…To evaluate performance, the SHOA is benchmarked against ACO, the GA, the particle swarm optimization algorithm, and the simulated annealing algorithm for the capacitated vehicle routing problem. …”
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A Bi-Objective Optimal Scheduling Method for the Charging and Discharging of EVs Considering the Uncertainty of Wind and Photovoltaic Output in the Context of Time-of-Use Electrici... by Xinfu Pang, Wen Jia, Haibo Li, Qingzhong Gao, Wei Liu

“…Finally, the model was solved using an improved multi-objective barebones particle swarm optimization algorithm. The simulation results show that the proposed scheduling strategy has a lower charging cost (CNY 11,032.4) and lower load mean square error (12.84 × 10⁵ kW²) than the strategy employed in the comparison experiment, which ensures the economic and stable operation of the microgrid.…”
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