Improved HLLL Lattice Basis Reduction Algorithm to Solve GNSS Integer Ambiguity by Kezhao Li, Chendong Tian, Yingxiang Jiao, Zhe Yue

“…In addition, when using the Householder image operator for orthogonalization, the old column norm is modified to obtain a new norm, reducing the number of column norm calculations. Compared with the LLL reduction algorithm and HLLL reduction algorithm, the experimental results show that the PHLLL algorithm has higher reduction efficiency and effectiveness. …”
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A Multipopulation PSO Based Memetic Algorithm for Permutation Flow Shop Scheduling by Ruochen Liu, Chenlin Ma, Wenping Ma, Yangyang Li

“…In this paper, a multipopulation particle swarm optimization (PSO) based memetic algorithm (MPSOMA) is proposed in this paper. In the proposed algorithm, the whole particle swarm population is divided into three subpopulations in which each particle evolves itself by the standard PSO and then updates each subpopulation by using different local search schemes such as variable neighborhood search (VNS) and individual improvement scheme (IIS). …”
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Routing algorithm for heterogeneous computing force requests based on computing first network by ZHANG Gang, LI Xi

“…The experiment verifies that algorithm has been optimized by an average of 8.85%, 15.51%, and 17.03% in terms of transmission success rate, convergence delay ratio, and load balancing compared to the IGAGCT algorithm and RBDQN algorithm, and 10.41%, 16.5%, and 16.81%, respectively from three aspects: heterogeneous request success rate, algorithms convergence delay rate, and load error rate of computing first networks.…”
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Hierarchical multi step Gray Wolf optimization algorithm for energy systems optimization by Idriss Dagal, AL-Wesabi Ibrahim, Ambe Harrison, Wulfran Fendzi Mbasso, Ahmad O. Hourani, Ievgen Zaitsev

“…To address these limitations, this paper introduces the Hierarchical Multi-Step Gray Wolf Optimization (HMS-GWO) algorithm. HMS-GWO incorporates a novel hierarchical decision-making framework that more closely mimics the observed hierarchical behavior of wolf packs, enabling each wolf type (Alpha, Beta, Delta, and Omega) to execute a structured multi-step search process. …”
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A Particle Swarm Optimization-Guided Ivy Algorithm for Global Optimization Problems by Kaifan Zhang, Fujiang Yuan, Yang Jiang, Zebing Mao, Zihao Zuo, Yanhong Peng

“…These enhancements collectively improve the algorithm’s ability to escape local optima and enhance the search stability. …”
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A Novel Multi-Objective Hybrid Evolutionary-Based Approach for Tuning Machine Learning Models in Short-Term Power Consumption Forecasting by Aleksei Vakhnin, Ivan Ryzhikov, Harri Niska, Mikko Kolehmainen

“…Experimental numerical results show the superiority of the proposed method compared to traditional tuning techniques, and random search, showcasing significant improvements in predictive accuracy and computational efficiency. …”
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A cyclic redundancy check aided encoding construction method for list sphere polar decoder by Wenbin Hu, Haiqiang Chen, Rui Wang, Qinhua Guo, Shuping Dang, Youming Sun, Xiangcheng Li

“…Simulation results show that the decoding performance of the proposed algorithm is comparable to that of the SD algorithm at medium and low code rates (with a difference of less than 0.2 dB), but it abandons the concept of search radius in the SD algorithm and does not require a backtracking process. …”
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Real number approximation by a rational number in the approximating k-ary algorithm by R.R. Enikeev

“…The time and memory complexity of these algorithms were shown; these methods were compared with respect to running time and iterations amount. …”
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Neighbourhood search-based metaheuristics for the bi-objective Pareto optimization of total weighted earliness-tardiness and makespan in a JIT single machine scheduling problem by Sona Babu, B.S. Girish

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Parameter Optimisation of Support Vector Machine using Genetic Algorithm for Cyberbullying Detection by Mohd Qorib Alqowiy, Ema Utami

“…To overcome this, various search algorithms have been suggested to optimize SVM parameters. …”
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WSN clustering routing algorithm based on PSO optimized fuzzy C-means by Aijing SUN, Shichang LI, Yicai ZHANG

“…Aimed at the problems of limited energy and unbalanced load in wireless sensor network, POFCA based on particle swarm optimization fuzzy C-means was proposed.POFCA was respectively optimized from the cluster stage and the data transmission stage.In the clustering stage, the particle swarm optimization fuzzy C-means was firstly used to overcome the sensitivity to the initial clustering center.And the cluster head was dynamically updated according to the remaining power and the relative distance of the nodes to balance the network load.Then in the data transfer phase, a path evaluation function was designed based on the distance factor, the energy factor and the nodal load.Besides, the cat swarm optimization was used to search the optimal routing path for the cluster head to balance the load of the cluster head without increasing the load of the relay node.The simulation result shows that compared with algorithms of LEACH and LEACH-improved, POFCA can effectively balance the network load, reduce the energy consumption of nodes and extend the lifetime of the entire network.…”
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Particle Swarm Optimization Algorithm with Multiple Phases for Solving Continuous Optimization Problems by Jing Li, Yifei Sun, Sicheng Hou

“…Based on the concept of building block thesis, a PSO algorithm with multiple phases was proposed to analyze the relation between search strategies and the solved problems. …”
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Joint beam hopping and coverage control optimization algorithm for multibeam satellite system by Guoliang XU, Feng TAN, Yongyi RAN, Feng CHEN

“…To improve the performance of multibeam satellite (MBS) systems, a deep reinforcement learning-based algorithm to jointly optimize the beam hopping and coverage control (BHCC) algorithm for MBS was proposed.Firstly, the resource allocation problem in MBS was transformed to a multi-objective optimization problem with the objective maximizing the system throughput and minimizing the packet loss rate of the MBS.Secondly, the MBS environment was characterized as a multi-dimensional matrix, and the objective problem was modelled as a Markov decision process considering stochastic communication requirements.Finally, the objective problem was solved by combining the powerful feature extraction and learning capabilities of deep reinforcement learning.In addition, a single-intelligence polling multiplexing mechanism was proposed to reduce the search space and convergence difficulty and accelerate the training of BHCC.Compared with the genetic algorithm, the simulation results show that BHCC improves the throughput of MBS and reduces the packet loss rate of the system, greedy algorithm, and random algorithm.Besides, BHCC performs better in different communication scenarios compared with a deep reinforcement learning algorithm, which do not consider the adaptive beam coverage.…”
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Hybrid Filter-Wrapper Feature Selection using Modified Flower Pollination Algorithm by Mohammad Ansari Shiri, Najme Mansouri

“…FWMBFPA achieves highest classification accuracy with the smallest number of selected features when compared to other algorithms when dealing with datasets with a large number of features.…”
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A Novel Center of Mass Optimization (CMO) Algorithm for Truss Design Problems by Hesam Varaee

“…In the proposed CMO, a random walk operator is introduced to enhance the exploitation capability of the CMO and help the search agents jump out of the local optimal. Mutation and elitism selection operators are also used to boost the overall performance of the proposed algorithm. …”
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An improved salp swarm algorithm for permutation flow shop vehicle routing problem by Yanguang Cai, Huajun Chen

“…Simulation results show that compared with simulated annealing, genetic algorithm and particle swarm optimization algorithm, the proposed algorithm has better optimization ability. …”
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Step-by-step classification detection algorithm of SPPM based on K-means clustering by Huiqin WANG, Wenbin HOU, Qingbin PENG, Minghua CAO, Rui HUANG, Ling LIU

“…In view of the high computational complexity in spatial pulse position modulation systems when using maximum likelihood detection algorithm, a step-by-step classification detection algorithm based on K-means clustering was proposed according to the characteristics of signal matrix with spatial pulse position modulation.The signal vector detection algorithm was utilized to detect the index of light source in the training samples.The on K-means clustering algorithm was utilized to acquire the mapping rule between centroid of samples and modulated symbol by offline training.Subsequently, online detection of modulated symbols was achieved based on the mapping rule, and then the index of light sources was detected by exhaustive search.In addition, Monte Carlo method was used to investigate the effects of key parameters such as the number of clusters and initialization times on the system bit error rate (BER) performance.Simulation results demonstrate that the proposed algorithm can achieve an approximate BER performance as the maximum likelihood algorithm on the basis of greatly reducing the computational complexity.Compared with the linear decoding algorithms, the proposed algorithm is also applicable to scenarios where the number of detectors is less than the number of light sources.…”
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Improved Ant Colony Algorithm for Network Flow Scheduling in SDN Data Center by ZHU Su-xia, LONG Yi-fei, SUN Guang-lu, LI Chun-feng

“…At present, in the software defined data center network, the flow scheduling strategy based on ant colony algorithm has the disadvantages of too slow convergence and search stagnation in path selection, which easily leads to the problems of excessively high data center network delay and low resource utilization.Therefore, this paper proposes an improved flow scheduling algorithm based on ant colony. …”
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Dynamic Multiobjective Optimization Algorithm Based on Average Distance Linear Prediction Model by Zhiyong Li, Hengyong Chen, Zhaoxin Xie, Chao Chen, Ahmed Sallam

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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

“…CC strategy is fairly effective to rapidly reduce the constraint violations during the evolutionary search process. 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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