A Fast Anti-Jamming Algorithm Based on Imitation Learning for WSN by Wenhao Zhou, Zhanyang Zhou, Yingtao Niu, Quan Zhou, Huihui Ding

“…First, on-network nodes obtain expert anti-jamming trajectories using heuristic algorithms, taking historical experiences into account. …”
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Genetic algorithm using a modified backward pass heuristic for the dynamic facility layout problem * by Ripon Kazi Shah Nawaz, Glette Kyrre, Koch Dirk, Hovin Mats, Torresen Jim

“…A computational study is performed with several existing heuristic algorithms. The experimental results show that the proposed algorithm is effective in dealing with the DFLP.…”
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A Comprehensive Multi-Strategy Enhanced Biogeography-Based Optimization Algorithm for High-Dimensional Optimization and Engineering Design Problems by Chenyang Gao, Teng Li, Yuelin Gao, Ziyu Zhang

“…To verify the performance of MSBBO, we compare it with standard BBO, seven BBO variants (PRBBO, BBOSB, HGBBO, FABBO, BLEHO, MPBBO and BBOIMAM) and seven meta-heuristic algorithms (GWO, WOA, SSA, ChOA, MPA, GJO and BWO) on multiple dimensions of 24 benchmark functions. …”
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A novel chaotic and neighborhood search-based artificial bee colony algorithm for solving optimization problems by Wen-sheng Xiao, Guang-xin Li, Chao Liu, Li-ping Tan

“…Abstract With the development of artificial intelligence, numerous researchers are attracted to study new heuristic algorithms and improve traditional algorithms. …”
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Optimal Design of Steel Structures Using Innovative Black Widow Algorithm Hybridized with Greedy Sensitivity-Based Particle Swarm Optimization Technique by Mohammad Reza Ghasemi, Abdolhamid Salarnia, Mehdi Ghasri

“…The effectiveness of the method focuses mainly on providing an intelligent population to enter meta-heuristic algorithms. As a meta-heuristic method in the second stage, the recently introduced Black Widow Optimization (BWO) algorithm was selected and improved by the authors. …”
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An Edge Server Placement Method Based on Reinforcement Learning by Fei Luo, Shuai Zheng, Weichao Ding, Joel Fuentes, Yong Li

“…In mobile edge computing systems, the edge server placement problem is mainly tackled as a multi-objective optimization problem and solved with mixed integer programming, heuristic or meta-heuristic algorithms, etc. These methods, however, have profound defect implications such as poor scalability, local optimal solutions, and parameter tuning difficulties. …”
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A Hierarchical Resource Scheduling Method for Satellite Control System Based on Deep Reinforcement Learning by Yang Li, Xiye Guo, Zhijun Meng, Junxiang Qin, Xuan Li, Xiaotian Ma, Sichuang Ren, Jun Yang

“…Compared with several other heuristic algorithms, the proposed approach is proven to have advantages in terms of CPU runtime, task completion rate, and resource variance between satellites. …”
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Dynamic Optimal Power Flow of Active Distribution Network Based on LSOCR and Its Application Scenarios by Weiqi Meng, Dongran Song, Xiaofei Deng, Mi Dong, Jian Yang, Rizk M. Rizk-Allah, Václav Snášel

“…Optimal power flow (OPF) is a crucial aspect of distribution network planning and operation. Conventional heuristic algorithms fail to meet the system requirements for speed and accuracy, while linearized OPF approaches are inadequate for distribution networks with high R/X ratios. …”
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Task scheduling in cloud computing environment using hybrid of genetic algorithm and naked mole rat algorithm (GA-NMRA) by Mohammad Ozaniezie Onasis

“…This research will compare hybrid Genetic Algorithm and Naked Mole Rat Algorithm (GA-NMRA) with other meta-heuristic algorithms. Other than that, this research will use Waterfall Model Methodology as its research methodology. …”
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Artificial Intelligence in Industry 4.0 and 5G Technology / by Vasant, Pandian, editor 366430, Munapo, Elias, editor 651363, Thomas, J. Joshua, 1973-, editor 651126, Weber, Gerhard-Wilhelm, editor 651125

“…Describes various AI approaches utilizing hybrid meta-heuristics optimization algorithms, including meta-search engines for innovative research and hyper-heuristics algorithms for performance measurement. Artificial Intelligence in Industry 4.0 and 5G Technology is a valuable resource for IT specialists, industry professionals, managers and executives, researchers, scientists, engineers, and advanced students an up-to-date reference to innovative computing, uncertainty management, and optimization approaches.…”

A Greedy Scheduling Approach for Peripheral Mobile Intelligent Systems by Ghassan Fadlallah, Djamal Rebaine, Hamid Mcheick

“…We conducted a comparison study with the longest cloudlet fact processing (LCFP), shortest cloudlet fact processing (SCFP), and Min-Min heuristic algorithms. The choice of those three algorithms is based on the efficiency and simplicity of their mechanisms, as reported in the literature, for allocating tasks to devices. …”
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WS-AWRE: Intrusion Detection Using Optimized Whale Sine Feature Selection and Artificial Neural Network (ANN) Weighted Random Forest Classifier by Omar Abdulkhaleq Aldabash, Mehmet Fatih Akay

“…Thus, to attain a high accuracy rate in ID, the present study proposes ML-based meta-heuristic algorithms, as these approaches possess innate merits of determining near-optimal solutions in limited time and are capable of dealing with multi-dimensional data. …”
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Dual‐stage day‐ahead optimized performance of renewable‐based microgrids by Ahmed M. Helmi, Farag K. Abo‐Elyousr, Hassan Haes Alhelou, Haitham S. Ramadan

“…Abstract In distributed networks, wind turbine generators (WTGs) are to be optimally sized and positioned for cost‐effective and efficient network service. Various meta‐heuristic algorithms have been proposed to allocate WTGs within microgrids. …”
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RVMF: RELIABLE ROUTING METHOD FOR VEHICULAR AD HOC NETWORKS USING MOTH-FLAME AND FIREFLY OPTIMIZATION ALGORITHMS by Soran A. Pasha

“…On the other hand, providing a suitable clustering method is NP-hard problems and meta-heuristic algorithms are suitable for solving these problems. …”
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Minimizing the Late Work of the Flow Shop Scheduling Problem with a Deep Reinforcement Learning Based Approach by Zhuoran Dong, Tao Ren, Jiacheng Weng, Fang Qi, Xinyue Wang

“…The experimental results show that the proposed RL and the combined method of RL+IG can obtain better solutions than other excellent heuristic and meta-heuristic algorithms in a short time.…”
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Improving Software Effort Estimation Models Using Grey Wolf Optimization Algorithm by Nada Mohammed Alsheikh, Nabil Mohammed Munassar

“…However, the COCOMO model has a limitation related to the precision of the software effort estimation. Meta-heuristic algorithms are preferred with parameter optimization because they can provide nearly optimal solutions at a reasonable cost. …”
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A state-of-the-Art review of heuristic and metaheuristic optimization techniques for the management of water resources by Vijendra Kumar, S. M. Yadav

“….; It identifies the importance and demerits of different heuristic and meta-heuristic algorithms.; It highlights some of the observations on the challenges and needs.; It highlights the state-of-the-art of water resources management and planning…”
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Enhancement of Classifier Performance Using Swarm Intelligence in Detection of Diabetes from Pancreatic Microarray Gene Data by Dinesh Chellappan, Harikumar Rajaguru

“…Subsequently, we applied meta-heuristic algorithms like the Dragonfly Optimization Algorithm (DOA) and Elephant Herding Optimization Algorithm (EHO) for feature selection. …”
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Percentile-Based Adaptive Immune Plasma Algorithm and Its Application to Engineering Optimization by Selcuk Aslan, Sercan Demirci, Tugrul Oktay, Erdal Yesilbas

“…Experimental studies showed that the percentile-based donor–receiver selection mechanism significantly contributed to the solving capabilities of the pIPA and helped it outperform well-known and state-of-art meta-heuristic algorithms.…”
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Optimal number and locations of automatic vehicle identification sensors considering link travel time estimation by Yiting Zhu, Zhaocheng He, Xinshao Zhang

“…The case in Shanghai shows the proposed game‐theoretic method is superior to other two heuristic algorithms. Moreover, compared to the real‐world sensor locations, the optimally redeployed locations improve both the estimation coverage and estimation accuracy. …”
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