A Novel Exploration Stage Approach to Improve Crayfish Optimization Algorithm: Solution to Real-World Engineering Design Problems by Harun Gezici

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Learning Improvement Heuristics for Multi-Unmanned Aerial Vehicle Task Allocation by Boyang Fan, Yuming Bo, Xiang Wu

“…In this paper, a NeuroSelect Discrete Particle Swarm Optimization (NSDPSO) algorithm is presented for the Multi-UAV Task Allocation (MUTA) problem. …”
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Adaptive Particle Swarm Optimization with Landscape Learning for Global Optimization and Feature Selection by Khalil Abbal, Mohammed El-Amrani, Oussama Aoun, Youssef Benadada

“…Particle swarm optimization (PSO), an important solving method in the field of swarm intelligence, is recognized as one of the most effective metaheuristics for addressing optimization problems. …”
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Reducing PAPR in NOMA Waveforms Using Genetic-Enhanced PTS and SLM: A Low-Complexity Approach for Improved throughput, power spectral density, and Power Efficiency by Arun Kumar, Nishant Gaur, Aziz Nanthaamornphong

“…To overcome these issues, this paper introduces new Particle Swarm Optimization (PSO)-improved partial transmit sequence (PTS) and Selective Mapping (SLM) schemes that optimally choose phase factors with much lower search complexity. …”
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Angular Random Walk Improvement of Resonator Fiber Optic Gyro by Optimizing Modulation Frequency by Wei Gao, Zhuo Wang, Guochen Wang, Weiqi Miao

“…In order to make this method effective, we use the particle swarm optimization algorithm to optimize the multi-parameter involved in ARW. …”
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Improved Firefly Algorithm Based on Heuristic Information by CUI Jia-rui, LI Qing, YANG Liu-yi, WANG Heng, ZHANG Bo-yu

“…Firefly Algorithm (FA) is an optimization algorithm based on swarm intelligence which mimics the social behavior of fireflies based on the flashing and attraction characteristics of fireflies With the aim to address the disadvantages of the firefly algorithm of slow convergence speed and ease of falling into the local optimum in the later period of the evolution process, the firefly algorithm is improved herein Two kinds of heuristic information are proposed into the algorithm to guide the convergence of the algorithm The first one takes the current global best as the heuristic information referencing the “global optimal” idea in particle swarm optimization, therefore, an algorithm called FAGO (Firefly Algorithm based on Global Optimization) is formed The second one is called FABE (Firefly Algorithm based on Bayesian Estimation) using the optimal moving direction calculated by Bayesian estimation as heuristic information The improved algorithms in this study are applied to numerical simulations of several classical test functions and compared with traditional FA and some other′s research are carried out The simulation results show that the proposed algorithms can well accelerate the convergence speed and improve the convergence accuracy…”
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An Adaptive Scheduling Method for Standalone Microgrids Based on Deep Q-Network and Particle Swarm Optimization by Borui Zhang, Bo Liu

“…Simulation results demonstrate that, under typical daily, high-volatility, and low-load scenarios, the proposed method improves clean energy utilization by 3.2%, 4.5%, and 10.9%, respectively, compared to conventional PSO algorithms while reducing power supply reliability risks to 0.70%, 1.04%, and 0.30%, respectively. …”
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System Identification of Heat-Transfer Process of Frequency Induction Furnace for Melting Copper Based on Particle Swarm Algorithm by Zhi-gang Jia, Xing-xuan Wang

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A Self-Adaptive Particle Swarm Optimization Based Multiple Source Localization Algorithm in Binary Sensor Networks by Long Cheng, Yan Wang, Shuai Li

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A New Hybrid Algorithm for Bankruptcy Prediction Using Switching Particle Swarm Optimization and Support Vector Machines by Yang Lu, Nianyin Zeng, Xiaohui Liu, Shujuan Yi

“…In this paper, a new hybrid algorithm combining switching particle swarm optimization (SPSO) and support vector machine (SVM) is proposed to solve the bankruptcy prediction problem. …”
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Joint Optimization of Multi-Period Empty Container Repositioning and Inventory Control Based on Adaptive Particle Swarm Algorithm by Jiaxin Cai, Ying Huang, Cuijie Diao, Zhihong Jin

“…Secondly, we design an adaptive particle swarm optimization algorithm, introduce dynamic inertia weight and acceleration coefficient adjustment mechanisms, and design heuristic rules for empty container repositioning. …”
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Presenting a Prediction Model for CEO Compensation Sensitivity using Meta-heuristic Algorithms (Genetics and Particle Swarm) by Saeed Khaljastani, Habib Piri, Reza Sotoudeh

“…Given these points, the aim of this research is to provide a model for predicting the sensitivity of CEO compensation using meta-heuristic algorithms, specifically genetic algorithms and particle swarm optimization. …”
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Hybridization of deep learning models with crested porcupine optimizer algorithm-based cybersecurity detection on industrial IoT for smart city environments by Sarah A. Alzakari, Mohammed Aljebreen, Mashael M. Asiri, Wahida MANSOURI, Sultan Alahmari, Mohammed Alqahtani, Shaymaa Sorour, Wafi Bedewi

“…To accomplish that, the CCPOA-HDLM method comprises distinct processes such as min-max normalization, improved Salp swarm algorithm (ISSA)-based feature selection, Multi-Channel Convolutional Neural Network - Recurrent Neural Network (MCNN-RNN)-based cybersecurity detection, and crested porcupine optimizer (CPO)-based parameter selection process. …”
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Fault Diagnosis of High-Power Tractor Engine Based on Competitive Multiswarm Cooperative Particle Swarm Optimizer Algorithm by Maohua Xiao, Weichen Wang, Kaixin Wang, Wei Zhang, Hengtong Zhang

“…First, the USB-CAN device was used to collect data of 8 items of the diesel engine under five different working conditions, and the data was parsed through the SAE J1939 protocol; then, the BP neural network was reconstructed, and a competitive multiswarm cooperative particle swarm optimizer algorithm (COM-MCPSO) was used to optimize its structure and weights. …”
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S-EPSO: A Socio-Emotional Particle Swarm Optimization Algorithm for Multimodal Search in Low-Dimensional Engineering Applications by Raynald Guilbault

“…The first proposed strategy assigns socio-emotional personalities to the particles forming the swarm. The analysis also introduces a technique to help them visit secluded zones. …”
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An Efficient Multisensor Hybrid Data Fusion Approach Based on Artificial Neural Networks and Particle Swarm Optimization Algorithms by Luc Eyembe Ihonock, Jean-François Dikoundou Essiben, Benjamin Salomon Diboma, Joe Suk Yong

“…The preprocessing stage of data is also included in the suggested technique, which starts with the design of the data-collecting device and ends with a hybrid model algorithm. Particle swarm optimization and artificial neural network methods are combined in the hybrid algorithm. …”
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Reliability Analysis of Three-Dimensional Slopes Considering the Soil Spatial Variability Based on Particle Swarm Optimization Algorithm by Xin Chen, Jiale Xu, Yukuai Wan, Rong Yang, Jiewen Sun, Di Wu

“…This paper presents a new algorithm for assessing the reliability of three-dimensional (3D) slope stability considering the spatial variability of soil based on the Particle Swarm Optimization (PSO) algorithm. …”
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Optimization method improvement for nonlinear constrained single objective system without mathematical models by HOU Gong-yu, XU Zhe-dong, LIU Xin, NIU Xiao-tong, WANG Qing-le

“…Therefore, to improve the optimization accuracy of nonlinear constrained single objective systems that are without accurate mathematical models while considering the cost of obtaining samples, a new method based on a combination of support vector machine and immune particle swarm optimization algorithm (SVM-IPSO) is proposed. …”
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A memetic algorithm for high‐strength covering array generation by Xu Guo, Xiaoyu Song, Jian‐tao Zhou, Feiyu Wang, Kecheng Tang, Zhuowei Wang

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Coverage path planning for multi-AUV considering ocean currents and sonar performance by Xukai Mu, Wei Gao

“…To address the CPP of multiple AUVs (multi-AUV) considering both sonar performance and ocean currents, we propose a new integrated algorithm based on the improved Dijkstra algorithm, Particle Swarm Optimization (PSO), and the ELKAI Solve. …”
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