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Journal articles on the topic 'Particle swan optimization'

Author: Grafiati

Published: 4 June 2025

Last updated: 15 July 2025

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1

Faseela, C. K., and Vennila H. "Economic and Emission Dispatch using Whale Optimization Algorithm (WOA)." International Journal of Electrical and Computer Engineering (IJECE) 8, no. 3 (2018): 1297–304. https://doi.org/10.11591/ijece.v8i3.pp1297-1304.

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This paper work present one of the latest meta heuristic optimization approaches named whale optimization algorithm as a new algorithm developed to solve the economic dispatch problem. The execution of the utilized algorithm is analyzed using standard test system of IEEE 30 bus system. The proposed algorithm delivered optimum or near optimum solutions. Fuel cost and emission costs are considered together to get better result for economic dispatch. The analysis shows good convergence property for WOA and provides better results in comparison with PSO. The achieved results in this study using th

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Memon, Ahsanullah, Mohd Wazir Bin Mustafa, Waqas Anjum, et al. "Dynamic response and low voltage ride-through enhancement of brushless double-fed induction generator using Salp swarm optimization algorithm." PLOS ONE 17, no. 5 (2022): e0265611. http://dx.doi.org/10.1371/journal.pone.0265611.

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A brushless double-fed induction generator (BDFIG) has shown tremendous success in wind turbines due to its robust brushless design, smooth operation, and variable speed characteristics. However, the research regarding controlling of machine during low voltage ride through (LVRT) need greater attention as it may cause total disconnection of machine. In addition, the BDFIG based wind turbines must be capable of providing controlled amount of reactive power to the grid as per modern grid code requirements. Also, a suitable dynamic response of machine during both normal and fault conditions needs

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He, Fang Guo. "Research on Information Applied Technology with Swarm Intelligence for the TSP Problem." Advanced Materials Research 886 (January 2014): 584–88. http://dx.doi.org/10.4028/www.scientific.net/amr.886.584.

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As a swarm intelligence algorithm, particle swarm optimization (PSO) has received increasing attention and wide applications in information applied technology. This paper investigates the application of PSO algorithm to the traveling salesman problem (TSP) on applied technology. Proposing the concepts of swap operator and swap sequence, we present a discrete PSO algorithm by redefinition of the equation for the particles velocity. A computational experiment is reported. The results show that the method proposed in this paper can achieve good results.

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He, Qi-Qiao, Cuiyu Wu, and Yain-Whar Si. "LSTM with particle Swam optimization for sales forecasting." Electronic Commerce Research and Applications 51 (January 2022): 101118. http://dx.doi.org/10.1016/j.elerap.2022.101118.

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Ling, Hai Feng, Zhuo Peng, Xun Lin Jiang, and Jian Tang. "A New Global Guides Selecting Strategy in Pareto Based MOPSO." Applied Mechanics and Materials 198-199 (September 2012): 1338–44. http://dx.doi.org/10.4028/www.scientific.net/amm.198-199.1338.

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In multi-objective particle swarm optimization (MOPSO), the selection of global guides for all partials is vital to improve the convergence and diversity of solutions. In this paper, the related work of global guides searching in MOPSO is introduced, and a new Pareto–based selecting strategy is proposed. Basing on the analysis of the structure and mapping relation of the particle swarm and the nondominated solutions archive, considering the density information, the global guides selecting frequency and other factors, a new gbest selecting strategy for each particle in the swam is presented. Ex

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Ahandan, Morteza Alinia, Hosein Alavi-Rad, and Nooreddin Jafari. "Frequency Modulation Sound Parameter Identification using Shuffled Particle Swarm Optimization." International Journal of Applied Evolutionary Computation 4, no. 4 (2013): 62–71. http://dx.doi.org/10.4018/ijaec.2013100104.

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The frequency modulation sound parameter identification is a complex multimodal optimization problem. This problem is modeled in the form of a cost function that is the sum-squared error between the samples of estimated wave and the samples of real wave. In this research, the authors propose a shuffled particle swarm optimization algorithm to solve this problem. In the shuffled particle swam optimization proposed here, population such as shuffled frog leaping algorithm is divided to several memeplexes and each memeplex is improved by the particle swam optimization algorithm. A comparison among

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Madhumala, R. B., Harshvardhan Tiwari, and Verma C. Devaraj. "Virtual Machine Placement Using Energy Efficient Particle Swarm Optimization in Cloud Datacenter." Cybernetics and Information Technologies 21, no. 1 (2021): 62–72. http://dx.doi.org/10.2478/cait-2021-0005.

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Abstract Efficient resource allocation through Virtual machine placement in a cloud datacenter is an ever-growing demand. Different Virtual Machine optimization techniques are constructed for different optimization problems. Particle Swam Optimization (PSO) Algorithm is one of the optimization techniques to solve the multidimensional virtual machine placement problem. In the algorithm being proposed we use the combination of Modified First Fit Decreasing Algorithm (MFFD) with Particle Swarm Optimization Algorithm, used to solve the best Virtual Machine packing in active Physical Machines to re

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Rahmad, B.Y. Syah, Muliono Rizki, Akbar Siregar Muhammad, and Elveny Marischa. "An efficiency metaheuristic model to predicting customers churn in the business market with machine learning-based." IAES International Journal of Artificial Intelligence (IJ-AI) 13, no. 2 (2024): 1547–56. https://doi.org/10.11591/ijai.v13.i2.pp1547-1556.

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Metaheuristics is an optimization method that improves and completes a task in a short period of time based on its objective function. The goal of metaheuristics is to search the search space for the best solution. Machine learning detects patterns in large amounts of data. Machine learning encourages enterprise automation in a variety of areas in order to improve predictive ability without requiring explicit programming to make decisions. The percentage of customers who leave the company or stop using the service is referred to as churn. The purpose of this research is to forecast customer ch

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Kumar, Amit, and T. V. Vijay Kumar. "Materialized View Selection Using Swap Operator Based Particle Swarm Optimization." International Journal of Distributed Artificial Intelligence 13, no. 1 (2021): 58–73. http://dx.doi.org/10.4018/ijdai.2021010103.

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The data warehouse is a key data repository of any business enterprise that stores enormous historical data meant for answering analytical queries. These queries need to be processed efficiently in order to make efficient and timely decisions. One way to achieve this is by materializing views over a data warehouse. An n-dimensional star schema can be mapped into an n-dimensional lattice from which Top-K views can be selected for materialization. Selection of such Top-K views is an NP-Hard problem. Several metaheuristic algorithms have been used to address this view selection problem. In this p

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Zhao, Wei, and Fei Li. "Collision Detection Based on Surface Simplification and Particle Swam Optimization." Advanced Materials Research 267 (June 2011): 476–81. http://dx.doi.org/10.4028/www.scientific.net/amr.267.476.

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We present an efficient stochastic collision detection based on surface simplification and particle swam optimization (PSO). In this framework, first, the search space is reduced by surface simplification during the pre-process and then the interference triangles are gained by PSO. This framework takes the surface simplification’s advantage of decreasing the triangles dramatically with little geometry error. In order to handle every collision detection step, we use surface simplification and PSO, by which user not only can balance performance and detection quality, but also increase the speed

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Huang, Chenn-Jung, Yi-Ta Chuang, and Kai-Wen Hu. "Using particle swam optimization for QoS in ad-hoc multicast." Engineering Applications of Artificial Intelligence 22, no. 8 (2009): 1188–93. http://dx.doi.org/10.1016/j.engappai.2009.03.004.

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Liu, Yi, and Sabina Shahbazzade. "The Berth-Quay Cranes and Trucks Scheduling Optimization Problem by Hybrid Intelligence Swam Algorithm." International Journal of Cognitive Informatics and Natural Intelligence 11, no. 2 (2017): 74–89. http://dx.doi.org/10.4018/ijcini.2017040105.

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Considered the cooperation of the container truck and quayside container crane in the container terminal, this paper constructs the model of the quay cranes operation and trucks scheduling problem in the container terminal. And the hybrid intelligence swarm algorithm combined the particle swarm optimization algorithm(PSO) with artificial fish swarm algorithm (AFSA) was proposed. The hybrid algorithm (PSO-AFSA) adopt the particle swarm optimization algorithm to produce diverse original paths, optimization of the choice nodes set of the problem, use AFSA's preying and chasing behavior improved t

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Selviani, Novi, and Joko Purwadi. "Support Vector Regression optimization with Particle Swam Optimization algorithm for predicting the gold prices." Bulletin of Applied Mathematics and Mathematics Education 3, no. 2 (2023): 55–62. http://dx.doi.org/10.12928/bamme.v3i2.9561.

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This paper discusses about how to predict the gold prices from 1 January 2021 to 31 January 2023. The method used in this study is the Support Vector Regression (SVR) technique, method that was developed from the support vector machine which is used as regression approach to predict future event. From the past study already know that SVR had limitation in achieving good performance because of its sensitivity to parameters. To overcome the SVR performance problems, an optimization algorithm is proposed in this study. The PSO algorithm is applied in this study to optimize the parameters of the S

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Rajalakshmi and P.Sumathy. "Spectrum Allocation in Cognitive Radio - Simplified Swarm Optimization Based Method." International Journal of Engineering and Advanced Technology (IJEAT) 9, no. 3 (2020): 1636–41. https://doi.org/10.35940/ijeat.C5439.029320.

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Communication through wireless mode is accelerated its expansion in broad manner that make a way to communicate with different type of computing devices to interact each other. As the number of users continues to increase, there is a constant demand for the usability of radio spectrum, which is a limited resource.Therefore a maximum utilization of spectrum is necessary at any moment. Moreover it is desired to share the capacity of the bandwidth between the user’s application on the basis of different channel utilization without compromising efficiency and fairness. Because cognitive syst

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Mohammed, Mohammed, Abduladhem Ali, and Mofeed Rashid. "Fuzzy Petri Net Controller for Quadrotor System using Particle Swam Optimization." Iraqi Journal for Electrical and Electronic Engineering 11, no. 1 (2015): 132–44. http://dx.doi.org/10.37917/ijeee.11.1.14.

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In this paper, fuzzy Petri Net controller is used for Quadrotor system. The fuzzy Petrinet controller is arranged in the velocity PID form. The optimal values for the fuzzy Petri Net controller parameters have been achieved by using particle swarm optimization algorithm. In this paper, the reference trajectory is obtained from a reference model that can be designed to have the ideal required response of the Quadrotor, also using the quadrotor equations to find decoupling controller is first designed to reduce the effect of coupling between different inputs and outputs of quadrotor. The system

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Armanto, Hendrawan, Harits Ar Rosyid, Muladi Muladi, and Gunawan Gunawan. "Utilization of the Particle Swam Optimization Algorithm in Game Dota 2." Register: Jurnal Ilmiah Teknologi Sistem Informasi 10, no. 2 (2024): 116–26. https://doi.org/10.26594/register.v10i2.3503.

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Dota 2, a Multiplayer Online Battle Arena game, is widely popular among gamers, with many attempting to create efficient artificial intelligence that can play like a human. However, current AI technology still falls short in some areas, despite some AI models being able to play decently. To address this issue, researchers continue to explore ways to enhance AI performance in Dota 2. This study focuses on the process of developing artificial intelligence code in Dota 2 and integrating the particle swarm optimization algorithm into Dota 2 Team's Desire. Although particle swarm optimization is an

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A. Ali, Abduladhem, Mohammed J. Mohammed, and Mofeed Turky Rashid. "Fuzzy Petri Net Controller for Quadrotor System using Particle Swam Optimization." Iraqi Journal for Electrical And Electronic Engineering 11, no. 1 (2015): 132–44. http://dx.doi.org/10.33762/eeej.2015.102735.

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Lohitha Lakshmi, K., P. Bhargavi, and S. Jyothi. "An Analysis of Breast Cancer DNA Sequences Using Particle Swam Optimization." International Journal of Engineering & Technology 7, no. 4.7 (2018): 335. http://dx.doi.org/10.14419/ijet.v7i4.7.20572.

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Conceptual Breast tumour conclusion, examination, and visualization are essential research challenges in Bioinformatics. Bosom tumour analysis incorporates recognizing of malignancy bumps and ordinary tissue. Investigation incorporates the present phase of the malignancy tissue and anticipation incorporates expectation of repeat of the bosom tumour in future ages in light of structure and game plan of the individual DNA succession. This paper investigations bosom disease DNA succession to anticipate event of bosom tumour utilizing Particle Swarm Optimization (PSO).PSO procedure is a populace b

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Kurniawati, Ika. "Deep Learning Model Based on Particle Swam Optimization for Buzzer Detection." Journal of Informatics Information System Software Engineering and Applications (INISTA) 7, no. 1 (2024): 22–32. https://doi.org/10.20895/inista.v7i1.1622.

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Along with the development of the internet, the presence of buzzers is increasingly widespread on social platforms, especially on Twitter. Buzzers have played an important role in influencing and spreading misinformation, manipulating public opinion, and harassing and intimidating online social media users. Therefore, an effective detection algorithm is needed to detect buzzer accounts that endanger social networks because they affect neutrality. In this research, we propose a Deep Neural Network model to detect buzzer accounts on Twitter. We conducted experiments on 1000 datasets using PSO-ba

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Lu, Jiansha, Lili Xu, Jinghao Jin, and Yiping Shao. "A Mixed Algorithm for Integrated Scheduling Optimization in AS/RS and Hybrid Flowshop." Energies 15, no. 20 (2022): 7558. http://dx.doi.org/10.3390/en15207558.

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The integrated scheduling problem in automated storage and retrieval systems (AS/RS) and the hybrid flowshop is critical for the realization of lean logistics and just-in-time distribution in manufacturing systems. The bi-objective model that minimizes the operation time in AS/RS and the makespan in the hybrid flowshop is established to optimize the problem. A mixed algorithm, named GA-MBO algorithm, is proposed to solve the model, which combines the advantages of the strong global optimization ability of genetic algorithm (GA) and the strong local search ability of migratory birds optimizatio

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Chang, Chen Yang, Jing Mei Zhai, Qin Xiang Xia, and Bin Cai. "Application of Particle Swarm Optimization Based on Support Vector Machine in Multi-Objective Structure Optimization." Applied Mechanics and Materials 201-202 (October 2012): 283–86. http://dx.doi.org/10.4028/www.scientific.net/amm.201-202.283.

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Aiming at addressing optimization problems of complex mathematical model with large amount of calculation, a method based on support vector machine and particle swarm optimization for structure optimization design was proposed. Support Vector Machine (SVM) is a powerful computational tool for problems with nonlinearity and could establish approximate structures model. Grey relational analysis was utilized to calculate the coefficient between target parameters in order to change the multi-objective optimization problem into a single objective one. The reconstructed models were solved by Particl

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Behera, Rabi Narayan, and Sujata Dash. "A Particle Swarm Optimization based Hybrid Recommendation System." International Journal of Knowledge Discovery in Bioinformatics 6, no. 2 (2016): 1–10. http://dx.doi.org/10.4018/ijkdb.2016070101.

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Due to rapid digital explosion user shows interest towards finding suggestions regarding a particular topic before taking any decision. Nowadays, a movie recommendation system is an upcoming area which suggests movies based on user profile. Many researchers working on supervised or semi-supervised ensemble based machine learning approach for matching more appropriate profiles and suggest related movies. In this paper a hybrid recommendation system is proposed which includes both collaborative and content based filtering to design a profile matching algorithm. A nature inspired Particle Swam Op

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Abdel-Rehim, Wael M. F. "Binary Particle Swarm Optimization Algorithm for Kidney Exchanges Acceleration using Parallel MATLAB." Egyptian Computer Science Journal 45, no. 2 (2021): 30–43. https://doi.org/10.5281/zenodo.4762451.

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In this paper, we implement a new method binary Particle Swarm Optimization (PSO) for solving the kidney exchange problem, which will improve the future decisions of kidney exchange programs. Because using a kidney exchange, we can help incompatible patient-donor couples to swap donors to receive a compatible kidney. Kidney paired donation programs provide an innovative approach for increasing the number of available kidneys. Further, we implementing binary particle swarm optimization in parallel with MATLAB with one, two, three and four threads and from the computations point of view, the aut

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FaZong Li, and GuiLi Yang. "Optimization of PID Controller Based on Particle Swam Algorithm for Automobile ABS." International Journal of Advancements in Computing Technology 4, no. 3 (2012): 50–58. http://dx.doi.org/10.4156/ijact.vol4.issue3.7.

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Emambocus, Bibi Aamirah Shafaa, Muhammed Basheer Jasser, Muzaffar Hamzah, Aida Mustapha, and Angela Amphawan. "An Enhanced Swap Sequence-Based Particle Swarm Optimization Algorithm to Solve TSP." IEEE Access 9 (2021): 164820–36. http://dx.doi.org/10.1109/access.2021.3133493.

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Lu, Huijuan, Bangjun Du, Jinyong Liu, Haixia Xia, and Wai K. Yeap. "A kernel extreme learning machine algorithm based on improved particle swam optimization." Memetic Computing 9, no. 2 (2016): 121–28. http://dx.doi.org/10.1007/s12293-016-0182-5.

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Lin, Cheng-Jian, Jun-Guo Wang, and Chi-Yung Lee. "Pattern recognition using neural-fuzzy networks based on improved particle swam optimization." Expert Systems with Applications 36, no. 3 (2009): 5402–10. http://dx.doi.org/10.1016/j.eswa.2008.06.110.

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Phanindra Kumar N.S.R. and Prasad Reddy P.V.G.D. "Evolutionary Image Thresholding for Image Segmentation." International Journal of Computer Vision and Image Processing 9, no. 1 (2019): 17–34. http://dx.doi.org/10.4018/ijcvip.2019010102.

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Image segmentation is a method of segregating the image into required segments/regions. Image thresholding being a simple and effective technique, mostly used for image segmentation, these thresholds are optimized by optimization techniques by maximizing the Tsallis entropy. However, as the two level thresholding extends to multi-level thresholding, the computational complexity of the algorithm is further increased. So there is need of evolutionary and swarm optimization techniques. In this article, first time optimal thresholds are obtained by maximizing the Tsallis entropy by using novel hyb

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Agustina, Deka, Yogi Isro Mukti, and Siti Muntari. "Integrasi Particle Swam Optimization Menggunakan K-Means untuk Klasterisasi Pengangguran di Kota Pagar Alam." Jurnal Pustaka AI (Pusat Akses Kajian Teknologi Artificial Intelligence) 3, no. 1 (2023): 34–41. http://dx.doi.org/10.55382/jurnalpustakaai.v3i1.543.

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Penelitian ini dilatar belakangi oleh masalah pengangguran yang masih signifikan di kota Pagar Alam yang akan berdampak pada kondisi ekonomi maupun sosial, semakin tinggi tingkat pengangguran menandakan masih adanya ketimpangan antara pencari kerja dan kesempatan kerja itu sendiri. Tujuan dari penelitian ini adalah untuk mengklasterisasi pengangguran di kota Pagar Alam menggunakan K-Means yang dioptimasi menggunakan Particle Swam Optimization (PSO). Metode data mining yang di gunakan yaitu Cross Industry Standered Process For Data Mining (CRIP-DM) yang terdiri dari 6 tahapan dimulai dari busss

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Hai, Le Xuan, Nguyen Van Thai, Vu Thi Thuy Nga, et al. "HIGH ORDER SLIDING MODE CONTROL WITH ANTI-SWAY BASED COMPENSATION ON ARTIFICIAL NEURAL NETWORK BY PSO ALGORITHM FOR OVERHEAD CRANE." Vietnam Journal of Science and Technology 55, no. 3 (2017): 347. http://dx.doi.org/10.15625/2525-2518/55/3/8617.

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This paper proposes a second order sliding mode controller combined with signal set calibrator for overhead crane tracking desired position and resisting disturbance. High order sliding mode controller ensures that the overhead crane tracks desired trajectory and resists disturbance. Neural network is trained by particle swarm optimization algorithm (PSO) to compensate anti-sway for load. The results on the computer simulation show that high order sliding mode controller with anti-sway compensation for overhead crane tracks desired trajectory and the swing of load that is smaller than high ord

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G, Sivagurunathan, Kotteeswaran R, Suresh M, and Kirthini Godweena A. "Design of centralized controller for multivariable process using MOPSO algorithm." Indian Journal of Science and Technology 14, no. 26 (2021): 2223–37. https://doi.org/10.17485/IJST/v14i26.539.

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Abstract <strong>Objective:</strong> To estimate centralized PID controller parameters for 4 outputs and 5 inputs crude distillation non-square system with RHP zeros process. <strong>Methods/Analysis:</strong> The Multi- Objective Particle Swam Optimization (MOPSO) algorithm is applied to determine the PID controller parameters for the considered distillation column process.<strong> Findings:</strong> The performance of the proposed controller is compared with two centralized controller schemes, Davison’s and Tanttu and Lieslehto methods. The Integral Square Erro

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Qiu, Wenzhen, Xingyu Song, Kaiyuan Shi, Xinshu Zhang, Zhiming Yuan, and Yunxiang You. "Multi-objective optimization of semi-submersible platforms using particle swam optimization algorithm based on surrogate model." Ocean Engineering 178 (April 2019): 388–409. http://dx.doi.org/10.1016/j.oceaneng.2019.02.039.

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Thobirin, Aris, and Iwan Tri Riyadi Yanto. "Automatic differentiation based for particle swarm optimization Steepest descent direction." International Journal of Advances in Intelligent Informatics 1, no. 2 (2015): 90. http://dx.doi.org/10.26555/ijain.v1i2.29.

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Particle swam optimization (PSO) is one of the most effective optimization methods to find the global optimum point. In other hand, the descent direction (DD) is the gradient based method that has the local search capability. The combination of both methods is promising and interesting to get the method with effective global search capability and efficient local search capability. However, In many application, it is difficult or impossible to obtain the gradient exactly of an objective function. In this paper, we propose Automatic differentiation (AD) based for PSODD. we compare our methods on

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Shingade, Sandip, Rajdeep Niyogi, and Mayuri Pichare. "Hybrid Particle Swarm Optimization-Jaya Algorithm for Team Formation." Algorithms 17, no. 9 (2024): 379. http://dx.doi.org/10.3390/a17090379.

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Collaboration in a network is crucial for effective team formation. This paper addresses challenges in collaboration networks by identifying the skills required for effective team formation. The communication cost is low when agents with the same skills are connected. Our main objective is to minimize team communication costs by selecting agents with the required skills. However, finding an optimal team is a computationally hard problem. This study introduces a novel hybrid approach called I-PSO-Jaya (improved PSO-Jaya, which combines PSO (Particle Swarm Optimization) and the Jaya algorithm wi

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Latifah, Nyayu Husni, Ade Silvia, Siti Nurmaini, Falah Yuridho, and Irsyadi Yani. "Swarm Robot Implementation in Gas Searching Using Particle Swarm Optimization Algorithm." Computer Engineering and Applications Journal 6, no. 3 (2017): 127–38. http://dx.doi.org/10.18495/comengapp.v6i3.221.

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In this reseach, a PSO method is impelemented in searching a gas leakage.Â A swarm robot consisted of 3 agents, yelow, blue, and green, was used.Â The research was done in 2 type of experiments, i.e. in simulation and real expeiment.Â A Matlab is used as a simulation validation while for the real experiment, a 2 x 2 m arena is used.Â From the experiment,Â it can be concluded that a good performance of a swam can be achieved using PSO method.

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Senthil Kumar Thangavel, K. Pavithra, G. Radhamani,. "IMPROVED PARTICLE SWAM OPTIMIZATION FOR CROWD SIMULATION USING HYBRID AGENT REINFORCEMENT LEARNING ALGORITHM." INFORMATION TECHNOLOGY IN INDUSTRY 9, no. 2 (2021): 144–54. http://dx.doi.org/10.17762/itii.v9i2.318.

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In an emergency route planning technique, simulating the dynamic crowd has route capacity constraints and global target of evacuating all crowd evacuees. To stimulate the crowd, the new arena is developed to know the real-time situation to face the crowd evacuation on exit point. The crowd evacuation is done with the process of Hybrid Agent Reinforcement Learning (HARL) algorithm consisting of Improved Multi-Agent Reinforcement Learning (IMARL) and State-Action-Reward-State-Action (SARSA). In the proposed work, the appropriate route selection mechanism focused on finding optimum evacuation rou

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Mowla, Alireza, and Nosrat Granpayeh. "A NOVEL DESIGN APPROACH FOR ERBIUM-DOPED FIBER AMPLIFIERS BY PARTICLE SWAM OPTIMIZATION." Progress In Electromagnetics Research M 3 (2008): 103–18. http://dx.doi.org/10.2528/pierm08061003.

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Sungthong, Aekarin, and Wudhichai Assawinchaichote. "Particle Swam Optimization Based Optimal PID Parameters for Air Heater Temperature Control System." Procedia Computer Science 86 (2016): 108–11. http://dx.doi.org/10.1016/j.procs.2016.05.027.

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Lai, Zhounian, Qian Li, An Zhao, Wenjie Zhou, Hailiang Xu, and Dazhuan Wu. "Improving Reliability of Pumps in Parallel Pump Systems Using Particle Swam Optimization Approach." IEEE Access 8 (2020): 58427–34. http://dx.doi.org/10.1109/access.2020.2980396.

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LING, SAI HO, FRANK JIANG, HUNG T. NGUYEN, and KIT YAN CHAN. "HYBRID FUZZY LOGIC-BASED PARTICLE SWARM OPTIMIZATION FOR FLOW SHOP SCHEDULING PROBLEM." International Journal of Computational Intelligence and Applications 10, no. 03 (2011): 335–56. http://dx.doi.org/10.1142/s1469026811003136.

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This paper, proposes a hybrid fuzzy logic-based particle swarm optimization (PSO) with cross-mutated operation method for the minimization of makespan in permutation flow shop scheduling problem. This problem is a typical non-deterministic polynomial-time (NP) hard combinatorial optimization problem. In the proposed hybrid PSO, fuzzy inference system is applied to determine the inertia weight of PSO and the control parameter of the proposed cross-mutated operation by using human knowledge. By introducing the fuzzy system, the inertia weight becomes adaptive. The cross-mutated operation effecti

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Tan, Minh Phan, Trieu Ha Phu, Long Duong Thanh, and Trung Nguyen Thang. "Improved particle swarm optimization algorithms for economic load dispatch considering electric market." International Journal of Electrical and Computer Engineering (IJECE) 10, no. 4 (2020): 3918–26. https://doi.org/10.11591/ijece.v10i4.pp3918-3926.

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Economic load dispatch problem under the competitive electric market (ELDCEM) is becoming a hot problem that receives a big interest from researchers. A lot of measures are proposed to deal with the problem. In this paper, three versions of PSO method such as conventional particle swarm optimization (PSO), PSO with inertia weight (IWPSO) and PSO with constriction factor (CFPSO) are applied for handling ELDCEM problem. The core duty of the PSO methods is to determine the most optimal power output of generators to obtain total profit as much as possible for generation companies without violation

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Y. Syah, Rahmad B., Rizki Muliono, Muhammad Akbar Siregar, and Marischa Elveny. "An efficiency metaheuristic model to predicting customers churn in the business market with machine learning-based." IAES International Journal of Artificial Intelligence (IJ-AI) 13, no. 2 (2024): 1547. http://dx.doi.org/10.11591/ijai.v13.i2.pp1547-1556.

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Metaheuristics is an optimization method that improves and completes a task in a short period of time based on its objective function. The goal of metaheuristics is to search the search space for the best solution. Machine learning detects patterns in large amounts of data. Machine learning encourages enterprise automation in a variety of areas in order to improve predictive ability without requiring explicit programming to make decisions. The percentage of customers who leave the company or stop using the service is referred to as churn. The purpose of this research is to forecast customer ch

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Sundararaman, KA, KP Padmanaban, and M. Sabareeswaran. "Optimization of machining fixture layout using integrated response surface methodology and evolutionary techniques." Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science 230, no. 13 (2015): 2245–59. http://dx.doi.org/10.1177/0954406215592920.

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Fixtures are the work-holding devices, widely used in manufacturing, to completely immobilize the workpiece during machining. The position of fixture elements around the workpiece strongly influences the workpiece deformation which in-turn affects the machining accuracy. The workpiece deformation can be minimized by finding the appropriate position for the locators and clamps. Thus, it is necessary to model the complex behavioral relationship that exists in the fixture–workpiece system. In this research paper, response surface methodology is used to model the relationship between position of l

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N.A., Akash Dutt Dubey. "Forecasting Gold Price using Particle Swam Optimization and Genetic Algorithm based Artificial Neural Networks." International Journal of Business Information Systems 1, no. 1 (2022): 1. http://dx.doi.org/10.1504/ijbis.2022.10049762.

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El-Ashmawi, Walaa H., Ahmed F. Ali, and Mohamed A. Tawhid. "An improved particle swarm optimization with a new swap operator for team formation problem." Journal of Industrial Engineering International 15, no. 1 (2018): 53–71. http://dx.doi.org/10.1007/s40092-018-0282-6.

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Zhang, Tao, and Xiaofei Li. "The Backpropagation Artificial Neural Network Based on Elite Particle Swam Optimization Algorithm for Stochastic Linear Bilevel Programming Problem." Mathematical Problems in Engineering 2018 (October 22, 2018): 1–9. http://dx.doi.org/10.1155/2018/1626182.

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For a class of stochastic linear bilevel programming problem, we firstly transform it into a deterministic linear bilevel covariance programming problem. Then, the deterministic bilevel covariance programming problem is solved by backpropagation artificial neural network based on elite particle swam optimization algorithm (BPANN-PSO). Finally, we perform the simulation experiments and the results show that the computational efficiency of the proposed algorithm has a potential upside compared with the classical algorithm.

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Joseph, Sunday Oyepata, and Talent Osarugue Osawaru. "The Impact of Fossil fuels and Agricultural Wastes Used as Energy Mix on Cement Production: Using Particle Swarm Optimization model." Journal of Energy Technology and Environment (NIPES) 4, no. 4 (2022): 12–20. https://doi.org/10.5281/zenodo.7443937.

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<em>Co-processing from industrial generated waste has been used in the Ordinary Portland cement production companies. In-depth examination of the effects of using conventional fuel (mineral coal, pet-coke, heavy oil, and natural gas) and agricultural waste is presented in this research, along with prospects for employing both for the optimization of cement production (sugar waste and ground nut shell). This mixture is meant to be used in a dry process rotary kiln for producing clinker. Particle Swarm Optimization was the optimization model employed (PSO). The results gotten from the (PSO) anal

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El-Ashmawi, Walaa H., Ahmed F. Ali, and Adam Slowik. "An improved Jaya algorithm with a modified swap operator for solving team formation problem." Soft Computing 24, no. 21 (2020): 16627–41. http://dx.doi.org/10.1007/s00500-020-04965-x.

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Abstract Forming a team of experts that can match the requirements of a collaborative task is an important aspect, especially in project development. In this paper, we propose an improved Jaya optimization algorithm for minimizing the communication cost among team experts to solve team formation problem. The proposed algorithm is called an improved Jaya algorithm with a modified swap operator (IJMSO). We invoke a single-point crossover in the Jaya algorithm to accelerate the search, and we apply a new swap operator within Jaya algorithm to verify the consistency of the capabilities and the req

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Sedighkia, Mahdi, and Bithin Datta. "Mitigating Nitrate Concentration Using an Optimal Cropping Pattern Developed by Linking a Soil and Water Assessment Tool with Evolutionary Optimization." Applied Sciences 13, no. 24 (2023): 13183. http://dx.doi.org/10.3390/app132413183.

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The unplanned development of agricultural land and urban areas poses threats to water quality, which can lead to the death of the aquatic species in rivers. The present study developed a novel framework by combining a soil and water assessment tool (SWAT) and evolutionary algorithms to optimize the cultivation pattern at the catchment scale in the Tajan River basin, with the aim of mitigating the environmental impacts of surface runoff from farms. This river basin is located in northern Iran, where quick agricultural development is one of the environmental challenges. We utilized a SWAT to sim

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Yang, Lin, and Yu Xia Pan. "Discrete Particle Swarm Optimization Algorithm for Lot-Streaming No-Wait Flow Shop Scheduling Problem." Advanced Materials Research 538-541 (June 2012): 863–68. http://dx.doi.org/10.4028/www.scientific.net/amr.538-541.863.

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This paper proposed discrete particle swarm optimization(DPSO) algorithm to solve lot-streaming no-wait flow shop scheduling problem(LNFSP) with the objective of the maximum completion time. The natural encoding scheme based on job permutation and newly-designed methods were adopted to produce new individuals . After the DPSO-based exploration, a efficient fast local search based on swap neighborhood structure is used to enhance the exploitation capability. Simulation results show the effectiveness of the proposed algorithms.

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