3D Flight Path Planning for UAV Based on Improved Particle Swarm Optimization Algorithm by Cunjie Li, Qingli Zhao, Canyi Che

“…The algorithm enhances early stage global search capability and later period local optimization capability compared to traditional particle swarm algorithm. …”
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Application of sparrow search swarm intelligence optimization algorithm in identifying the critical surface in slope-stability by Abhijit Saha

“…Results show SSA is a strong contender to bio-inspired methods like genetic algorithms, big-bang big-crunch, simulated annealing, and artificial bee colony algorithms. …”
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Research on Pump Down Treating Pressure Prediction Based on New Particle Swarm Optimization Algorithm by LIU Mingming, SONG Zhiyong, MA Shou, WEI Yuhua, ZHUANG Huanle

“…This paper proposes a pump down friction model based on the Bernoulli equation of interstitial flow, and establishes a mathematical model of pump down treating pressure based on drag friction and perforation friction, and further uses a new particle swarm algorithm and the least squares method to establish an automatic history matching method for pump down treating pressure. …”
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Stochastic Movement Swarm Performing a Coverage Task with Physical Parameters by Krzysztof FALKOWSKI, Michał DUDA

“…This paper describes an attempt of implementing physical parameters into a virtual swarm algorithm solution. It defines which physical parameters of the single object need to be known to properly transfer a virtual algorithm into a physical system. …”
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Hybrid Deep Neural Network Optimization with Particle Swarm and Grey Wolf Algorithms for Sunburst Attack Detection by Mohammad Almseidin, Amjad Gawanmeh, Maen Alzubi, Jamil Al-Sawwa, Ashraf S. Mashaleh, Mouhammd Alkasassbeh

“…Bio-inspired optimization algorithms, especially Particle Swarm Intelligence (PSO) and the Gray Wolf Optimizer (GWO), have been widely used to optimize complex tasks because of their ability to explore the search space and their fast convergence. …”
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Improved fish swarm algorithm based on chaos and its application in abnormal detection of industrial control network by Yaping WU, Hongzhao DONG, Yingying LIN, Jing LIU

“…Artificial fish swarm algorithm as a new type of bionic swarm intelligence optimization algorithm has been successfully used in a variety of optimization problems and practical engineering field,but when faced with the complex optimization problems,especially the multiple extreme value of peak and multimodal function optimization problems,due to the objective function has many local minima,inevitably there are defects such as premature and slow convergence speed.The random and ergodic theory was introduced into the basic artificial fish swarm algorithm,and an improved algorithm was constructed to make artificial fish swarm search avoid possible local extremum.The effectiveness of the algorithm was validated in the simulation application in industrial control network anomaly detection.Compared with the basic artificial fish school algorithm,the chaos improved artificial fish swarm algorithm can effectively avoid the long-term search of the algorithm near the local extreme value.The algorithm has better performance in terms of global convergence and the search efficiency is more prominent.…”
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Diversity Operators-based Artificial Fish Swarm Algorithm to Solve Flexible Job Shop Scheduling Problem by Alaa Wagih Abdulqader, Sura Mazin Ali

“… Artificial fish swarm algorithm (AFSA) is one of the critical swarm intelligent algorithms. …”
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Multimodal Transport Path Selection of Cold Chain Logistics Based on Improved Particle Swarm Optimization Algorithm by Changjiang Zheng, Kai Sun, Yuhang Gu, Jinxing Shen, Muqing Du

“…In establishing the path-selection model, customer satisfaction is introduced, which is reflected by arrival punctuality and the quality of CCF. An improved particle swarm optimization algorithm (IPSO) is introduced to address the model and is proven to retain a fast convergence rate and achieve outstanding solving accuracy through the experimental study. …”
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EVALUATION OF PARTICLE SWARM ALGORITHM MODIFICATIONS ON SUPPORT VECTOR MACHINE HYPERPARAMETER OPTIMIZATION TUNING FOR RAIN PREDICTION by Aina Latifa Riyana Putri, Joko Riyono, Christina Eni Pujiastuti, Supriyadi

“… The Particle Swarm Optimization (PSO) algorithm, though simple and effective, faces challenges like premature convergence and local optima entrapment. …”
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Improved Particle Swarm Optimization Based Distributed Energy-Efficient Opportunistic Algorithm for Clustering and Routing in WSNs by M. S. Sivagamasundari, T. Thamaraimanalan, S. Ramalingam, K. Balachander

“…The cluster aims to improve the network's scalability, energy efficiency, and other characteristics. The particle swarm optimization algorithm is modified to obtain energy-efficient WSNs. …”
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An Improved Salp Swarm Algorithm for Solving a Multi-Temperature Joint Distribution Route Optimization Problem by Yimei Chang, Jiaqi Yu, Yang Wang, Xiaoling Xie

“…To solve this model, an improved salp swarm algorithm (SSA) has been developed. The feasibility and effectiveness of both the proposed model and algorithm are demonstrated using R110 data from the Solomon standard calculation example. …”
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Parameter identification of sound absorption model of porous materials based on modified particle swarm optimization algorithm. by Xiaomei Xu, Ping Lin

“…In the aspect of computer running time, compared with the standard particle swarm optimization algorithm, the modified particle swarm optimization algorithm takes shorter running time. …”
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Behavioral Modeling of SiC MOSFET Static and Dynamic Characteristics Based on Particle Swarm Optimization Algorithm by Zhibo Zhu, Yang Zhao, Wei Yan

“…This study proposes a SiC MOSFET behavioral model with parasitic parameters based on the particle swarm optimization (PSO) algorithm. The PSO algorithm is used to extract the parameters of the improved Enz-Krummnacher-Vittoz model and a static characteristic behavioral model of the SiC MOSFET is established, avoiding a large amount of simulation or calculation time for circuit and datasheet-drive models, the efficiency is increased by 60%, and the model accuracy is 98% compared to the datasheet. …”
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A Multi-Goal Particle Swarm Optimizer for Test Case Prioritization by Muhammad Nazir, Arif Mehmood, Waqar Aslam, Yongwan Park, Gyu Sang Choi, Imran Ashraf

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Optimisation of engineering system using a novel search algorithm: the Spacing Multi-Objective Genetic Algorithm by L. Falahiazar, H. Shah-Hosseini

“…A large number of real-world issues are among difficult and multi-objective problems. Recently, it has been recognised that the evolutionary algorithms optimise well these types of problems. …”
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Hybridization of Swarm for Features Selection to Modeling Heart Attack Data by Omar Shakir, Ibrahim Saleh

“…The proposed method to select features has been compared with each of the three swarms, and the Catboost algorithm has been compared to traditional classification algorithms (naive Bayes, decision trees). …”
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A Hybrid K-Means++ and Particle Swarm Optimization Approach for Enhanced Document Clustering by Eisha Hassan, Fazila Malik, Qazi Waqas Khan, Nadeem Ahmad, Muhammad Sardaraz, Faten Khalid Karim, Hela Elmannai

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Calculation of Neural Network Weights and Biases Using Particle Swarm Optimization by Jerin Paul Selvan, Girish Pandurang Potdar

“…Various machine learning techniques and algorithms have been used to address, and are still being used to tackle, several real-world issues. …”
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Hybrid salp swarm maximum power point tracking algorithm for photovoltaic systems in highly fluctuating environmental conditions by Mohd Nasrul Izzani Jamaludin, Mohammad Faridun Naim Tajuddin, Tarek Younis, Sudhakar Babu Thanikanti, Mohammad Khishe

“…This paper proposes a hybrid MPPT algorithm that combines the benefits of the salp swarm algorithm (SSA) and hill climbing (HC) techniques. …”
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A Comparative Study of Particle Swarm Optimization and Artificial Bee Colony Algorithm for Numerical Analysis of Fisher’s Equation by Geeta Arora, Kiran Bala, Homan Emadifar, Masoumeh Khademi

“…The two optimization techniques, namely, particle swarm optimization (PSO) and artificial bee colony (ABC), have been compared for the numerical results in terms of errors, which are employed to find the shape parameter of the RBF. …”
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