Multi-Agent Meta Reinforcement Learning for Reliable and Low-Latency Distributed Inference in Resource-Constrained UAV Swarms by Marwan Dhuheir, Aiman Erbad, Bechir Hamdaoui, Samir Brahim Belhaouari, Mohsen Guizani, Thang X. Vu

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Dynamic performance improvement of oscillating water column wave energy conversion system using optimal walrus optimization algorithm-based control strategy by Habiba A. ElDemery, Hany M. Hasanien, Mohammed Alharbi, Chuanyu Sun, Dina A. Zaky

“…The proposed WOA-based PI controller design’s effectiveness is evaluated by comparing its simulation results with that obtained from using genetic algorithm (GA), grey wolf (GWO), particle swarm (PWO), and harmony search (HS) optimization-based PI controllers under symmetrical and unsymmetrical faults. …”
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LEADERS AND FOLLOWERS ALGORITHM FOR TRAVELING SALESMAN PROBLEM by Helen Yuliana Angmalisang, Syaiful Anam

“…Leaders and Followers algorithm is a metaheuristics algorithm. In solving continuous optimization, this algorithm is proved to be better than other well-known algorithms, such as Genetic Algorithm and Particle Swarm Optimization. …”
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Improved Coyote Optimization Algorithm for Optimally Installing Solar Photovoltaic Distribution Generation Units in Radial Distribution Power Systems by Thang Trung Nguyen, Thai Dinh Pham, Le Chi Kien, Le Van Dai

“…Furthermore, we have also applied five other metaheuristic algorithms consisting of biogeography-based optimization (BBO), genetic algorithm (GA), particle swarm optimization algorithm (PSO), sunflower optimization (SFO), and salp swarm algorithm (SSA) for dealing with the same problem and evaluating further performance of ICOA. …”
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Application of Harris Hawks Optimization Algorithm in Optimization of Generalized Nonlinear Muskingum Parameters ——A Case Study of the Luohe River by CHEN Haitao, ZHAO Zhijie

“…The Muskingum model plays an important role in river flood simulation,and its simulation accuracy relies on the optimal selection of parameters.To address the current challenges in parameter calibration for the Muskingum model,such as complex solution processes and low accuracy,the use of the Harris Hawks optimization (HHO) algorithm was proposed to optimize its parameters.HHO algorithm has a wide range of global search capabilities,with fewer parameters to be adjusted.Taking Luohe River,a tributary of the Yellow River,as the research object,the generalized nonlinear Muskingum model was used to simulate the flood in the Yiyang-Baimasi section of the river.The parameters were optimized by employing the HHO algorithm,particle swarm optimization (PSO) algorithm,and ant colony optimization (ACO) algorithm,respectively.The results show that the generalized nonlinear Muskingum model based on the HHO algorithm achieved high simulation accuracy in the Yiyang-Baimasi section of the Luohe River,with a Min.SSD of 1 237 and the flood peak error (DPO) of only 5,outperforming those obtained through optimization using PSO algorithm and ACO algorithm.The results are suitable for application in flood forecasting in the Yiyang-Baimasi section of the Luohe River.…”
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An innovative coverage optimization method for smart information monitoring in agricultural IoT using the multi-strategy Pelican optimization algorithm by Wei Chen, Qike Cao, Bingyu Cao, Bo Jin

“…Comparative experiments with Improved Artificial Bee Colony Algorithm (IABC), Chaotic Adaptive Firefly Optimization Algorithm (CAFA), Adaptive Particle Swarm Optimization (APSO), and Lévy Flight Strategy Chaotic Snake Optimization Algorithm (LCSO) demonstrate that MSPOA improves network coverage by 5.85%, 11.33%, 21.05%, and 20.66%, respectively. …”
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A Novel Optimization Algorithm Inspired by Egyptian Stray Dogs for Solving Multi-Objective Optimal Power Flow Problems by Mohamed H. ElMessmary, Hatem Y. Diab, Mahmoud Abdelsalam, Mona F. Moussa

“…The proposed technique is compared with the particle swarm optimization (PSO), multi-verse optimization (MVO), grasshopper optimization (GOA), and Harris hawk optimization (HHO) and hippopotamus optimization (HO) algorithms through MATLAB simulations by applying them to the IEEE 30-bus system under various operational circumstances. …”
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Modulation optimization method for seven-level SHEPWM inverter based on EPSO algorithm by Renzheng Wang, Yuncheng Zhang, Ying Chen, Zhenyao Xin, Di Fan

“…In this paper, a modulation optimization method for seven-level SHEPWM inverter based on the Evolutionary Particle Swarm Optimization (EPSO) algorithm is proposed to address this problem, so that the algorithm quickly converges to the global optimum solution. …”
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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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Optimization of Planning Layout of Urban Building Based on Improved Logit and PSO Algorithms by Yun Li, Yanping Chen, Miaoxi Zhao, Xinxin Zhai

“…The particle in the particle swarm is assigned to the index parameter of logit model, and then the logit model in the evaluation system is run. …”
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Energy-efficient artificial fish swarm-based clustering protocol for enhancing network lifetime in underwater wireless sensor networks by Puneet Kaur, Kiranbir Kaur, Kuldeep Singh, Kiran Saleem, Ateeq Ur Rehman, Rupesh Gupta, Seada Hussen Adem

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Multiobjective Optimization of Irreversible Thermal Engine Using Mutable Smart Bee Algorithm by M. Gorji-Bandpy, A. Mozaffari

“…The results have been checked with some of the most common optimizing algorithms like Karaboga’s original artificial bee colony, bees algorithm (BA), improved particle swarm optimization (IPSO), Lukasik firefly algorithm (LFFA), and self-adaptive penalty function genetic algorithm (SAPF-GA). …”
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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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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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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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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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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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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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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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Commercial Vehicle Ride Comfort Optimization Based on Intelligent Algorithms and Nonlinear Damping by Shuilong He, Keren Chen, Enyong Xu, Wei Wang, Zhansi Jiang

“…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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