Article
Multidisciplinary Sciences
Zhi-guang Guo, Yong-fu Liu, Chang-jiang Ao
Summary: This paper proposes a solution to the chaotic concrete dispatching in construction, which leads to waste, and validates its feasibility through a calculation example. The solution provides more appropriate dispatching and distribution, meeting the needs of the worksite and mixing station, and improving project management.
SCIENTIFIC REPORTS
(2022)
Article
Multidisciplinary Sciences
Xinru Li, Zihan Lin, Haoxuan Lv, Liang Yu, Ali Asghar Heidari, Yudong Zhang, Huiling Chen, Guoxi Liang
Summary: This paper proposes an improved algorithm named PSMADE, which integrates the differential evolution algorithm and the Powell mechanism to overcome the limitations of the original slime mould algorithm. Experimental results demonstrate that PSMADE exhibits outstanding performance in solving complex problems and shows potential as an effective problem-solving tool.
Article
Biology
Mohammed A. Awadallah, Mohammed Azmi Al-Betar, Malik Shehadeh Braik, Abdelaziz Hammouri, Iyad Abu Doush, Raed Abu Zitar
Summary: An enhanced binary version of the Rat Swarm Optimizer (RSO) is proposed for Feature Selection (FS) problems, showing superior performance over other methods on some datasets.
COMPUTERS IN BIOLOGY AND MEDICINE
(2022)
Article
Mathematics
Rizk M. Rizk-Allah, Hatem Abdulkader, Samah S. Abd Elatif, Diego Oliva, Guillermo Sosa-Gomez, Vaclav Snasel
Summary: This paper presents a binary hybridization algorithm based on the mathematical procedures of the grey wolf optimizer and particle swarm optimization for the cryptanalysis of S-AES. The proposed BPSOGWO algorithm is more accurate and provides superior results compared to other methods, improving the cryptanalysis accuracy of S-AES by a significant percentage. It also reduces the search space and shows promising potential in attacking the key employed in the S-AES cipher.
Article
Energy & Fuels
Aml Sayed, Mohamed Ebeed, Ziad M. Ali, Adel Bedair Abdel-Rahman, Mahrous Ahmed, Shady H. E. Abdel Aleem, Adel El-Shahat, Mahmoud Rihan
Summary: The paper proposes a hybrid optimization technique, MPSO-EO, to solve the unit commitment problem (UCP) under deterministic and stochastic load demand, which outperforms standard EO with significant cost savings. The simulation results demonstrate the fairly good performance of MPSO-EO in solving UCP compared to standard EO and other reported techniques.
Article
Computer Science, Information Systems
Tianxi Ma, Yunhe Wang, Xiangtao Li
Summary: In this paper, a Convex Combination Multiple Populations Competitive Swarm Optimization algorithm (CDCSO) is proposed to solve the complex search optimization problem of UAVs searching for a moving target. The algorithm combines multiple populations and a novel convex combination update strategy to prevent falling into local optima and improves performance.
INFORMATION SCIENCES
(2023)
Article
Biotechnology & Applied Microbiology
Zongshan Wang, Hongwei Ding, Jingjing Yang, Peng Hou, Gaurav Dhiman, Jie Wang, Zhijun Yang, Aishan Li
Summary: This paper introduces a bio-inspired algorithm called Salp swarm algorithm (SSA) and proposes an improved strategy combining pinhole-imaging-based learning (PIBL) and orthogonal experimental design (OED). It also designs an effective adaptive conversion parameter method to enhance the algorithm's performance. Comparative experiments show that the algorithm performs well in most benchmark problems.
FRONTIERS IN BIOENGINEERING AND BIOTECHNOLOGY
(2022)
Article
Chemistry, Multidisciplinary
Fu- Chou, Tian-Hsiang Huang, Po-Yuan Yang, Chin-Hsuan Lin, Tzu-Chao Lin, Wen-Hsien Ho, Jyh-Horng Chou
Summary: The improved method effectively optimizes the fractional-order particle swarm optimizer, and comparative experiments show its superiority over traditional particle swarm optimizers. The final verification demonstrates the reliability and effectiveness of this method in a heart disease prediction application.
APPLIED SCIENCES-BASEL
(2021)
Article
Metallurgy & Metallurgical Engineering
Luo Yi-jian, Cui Yi-an, Xie Jing, Lu He-shun-zi, Liu Jian-xin
Summary: Particle swarm optimization (PSO) is used to invert simple geometric self-potential anomalies, achieving remarkable results through comprehensive exploration of the search space with different particle behaviors. Additionally, six improved PSOs are introduced for the inversion of the cylinder model, effectively improving inversion accuracy and convergence speed as verified by numerical experiments.
JOURNAL OF CENTRAL SOUTH UNIVERSITY
(2021)
Article
Computer Science, Artificial Intelligence
Musa Dogan, Ilker Ali Ozkan
Summary: To increase the market value and quality of wheat, it is important to use the visual properties of durum and bread wheat to separate different types and determine the amount of foreign matter. This study used the extreme learning machine (ELM) algorithm to classify wheat kernels and foreign matter based on image features. The feature selection process was applied to remove irrelevant features, and the ELM model was improved using the novel Harris hawks' optimizer (HHO) and the particle swarm optimizer (PSO). Compared to other models, the optimized ELM models showed good stability and accuracy, achieving 99.32% in binary classification and 95.95% in multi-class classification.
NEURAL COMPUTING & APPLICATIONS
(2023)
Article
Computer Science, Artificial Intelligence
Fei Li, Qiang Yue, Yuanchao Liu, Haibin Ouyang, Fangqing Gu
Summary: This paper proposes a fast density peak clustering based particle swarm optimizer (DPCPSO) to solve dynamic optimization problems (DOPs). DPCPSO addresses DOPs by applying fast density peak clustering to create multiple sub-populations, using stagnation detection to handle loss of diversity, and proposing an optimal particle calibration strategy for environmental changes. Experimental results demonstrate that the proposed algorithm performs competitively in solving DOPs.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Chi Ma, Haisong Huang, Qingsong Fan, Jianan Wei, Yiming Du, Weisen Gao
Summary: This paper proposes an improved grey wolf optimizer algorithm based on the Aquila Optimizer, which can enhance the global search ability and balance the exploration and exploitation stages.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
Article
Computer Science, Interdisciplinary Applications
Zhao He, Tanghong Liu, Hui Liu
Summary: Improved particle swarm optimization algorithms are proposed to enhance the efficiency of aerodynamic optimization for the head shape of high-speed trains. The OPT-LSSVR model and the EMPSO algorithm demonstrate improved prediction accuracy and multi-objective optimization efficiency respectively.
ADVANCES IN ENGINEERING SOFTWARE
(2022)
Article
Computer Science, Artificial Intelligence
Diana Cristina Valencia-Rodriguez, Carlos A. Coello Coello
Summary: Particle Swarm Optimization (PSO) is a bio-inspired metaheuristic algorithm that utilizes information exchange between particles to explore the search space. This study focuses on the influence of the number of connections among particles in Multi-Objective Particle Swarm Optimizers (MOPSOs) using random regular graphs as the swarm topology. Experimental results indicate that a higher connection degree can lead to algorithm instability in various problems, and MOPSOs with the same connection degree exhibit similar behavior.
SWARM AND EVOLUTIONARY COMPUTATION
(2023)
Review
Computer Science, Interdisciplinary Applications
Jeffrey O. O. Agushaka, Absalom E. E. Ezugwu, Laith Abualigah, Samaher Khalaf Alharbi, Hamiden Abd El-Wahed Khalifa
Summary: In this paper, a comprehensive comparison was conducted to evaluate the impact of population size, number of iterations, and different initialization methods on the performance of population-based metaheuristic optimizers. The results indicated that population size and number of iterations affect the algorithm performance, and certain algorithms are sensitive to the initialization schemes. Good population diversity and a suitable number of iterations are likely to lead to optimal solutions.
ARCHIVES OF COMPUTATIONAL METHODS IN ENGINEERING
(2023)
