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Journal articles on the topic 'Genetic Particle Swarm Optimization'

Author: Grafiati

Published: 3 June 2025

Last updated: 31 July 2025

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1

Ariyati, Miftah Rahmalia, and Ahmad Reza Musthafa. "Autonomous Robot Path Planning Menggunakan Perbandingan Metode Particle Swarm Optimization dan Genetic Algorithm." Jurnal Buana Informatika 9, no. 2 (2018): 61. http://dx.doi.org/10.24002/jbi.v9i2.1518.

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Abstract. A research on robot planning path has been widely conducted and developed. Generally, the desired path is the safe one which has no obstacles and it can be conducted in a quick process. There are several methods that can be applied in planning the path including particle swarm optimization method and genetic algorithm. Both methods are compared in this research in order to discover the best method. Particle swarm optimization method utilizes the particle population movement and genetic algorithm method explores a population consisting individuals’ solutions. The finding reveals that

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Gong, Yue-Jiao, Jing-Jing Li, Yicong Zhou, et al. "Genetic Learning Particle Swarm Optimization." IEEE Transactions on Cybernetics 46, no. 10 (2016): 2277–90. http://dx.doi.org/10.1109/tcyb.2015.2475174.

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Fan, Jin Wei, Qin Mei, and Xiao Feng Wang. "Robust PID Parameters Optimization Design Based on Improved Particle Swarm Optimization." Applied Mechanics and Materials 373-375 (August 2013): 1125–30. http://dx.doi.org/10.4028/www.scientific.net/amm.373-375.1125.

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The article, based on satisfying robustness of the system and put forward the objective function of time-domain performance and dynamic characteristics, introduced genetic operators into Particle Swarm Optimization. The algorithm improve the diversity of particles by selection and hybridization operations and strengthen the excellent characteristics of particles in the swarm by introducing crossover and mutation genes, which can avoid bog down into local optima and premature convergence and enhance searching efficiency. The simulation results indicate that when the algorithm is applied to the

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Zhang, Quan, Yichong Dong, Yan Peng, Jun Luo, Shaorong Xie, and Huayan Pu. "Asymmetric Bouc–Wen hysteresis modeling and inverse compensation for piezoelectric actuator via a genetic algorithm–based particle swarm optimization identification algorithm." Journal of Intelligent Material Systems and Structures 30, no. 8 (2019): 1263–75. http://dx.doi.org/10.1177/1045389x19831360.

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The hysteresis characteristics, which commonly existed in smart materials–based actuators, play a significant role in precision control technology. In this article, a modified Bouc–Wen model which can describe the asymmetric hysteresis characteristics of piezoelectric ceramic actuators is investigated. The corresponding parameters of the modified Bouc–Wen hysteresis model are identified through a genetic algorithm–based particle swarm optimization algorithm. Compared with independent particle swarm optimization method which is easily trapped in the local extremum, the proposed genetic algorith

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Tchikdje, K. Marthe P., Alain Kalameu, Bonaventure Djeumako, Bienvenu Kenmeugne, and Djidda Mahamat Annouar. "Improved Particle Swarm Optimization for the Determination of Chaboche Model Parameters of the Elastoplastic Behavior Railway Steel." Improved Particle Swarm Optimization for the Determination of Chaboche Model Parameters of the Elastoplastic Behavior Railway Steel 8, no. 12 (2024): 10. https://doi.org/10.5281/zenodo.10488254.

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This paper presents new Particle Swarm Optimization algorithm for the determination of Chaboche model parameter. This is based on the reduction of search-space where the optimal parametersare belonged. The obtained results are compared to other metaheuristic approaches mainly the Genetic Algorithm and standard Particle Swarm Optimization by using the Mean Square Error and optimization time as criteria.The first yielded0.316 for a new approach. Despite this efficiency, the proposed approach has the highest optimization time, which is 787 seconds against 712 seconds for a standard Particle Swarm

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Zhu, Hao, Yumei Hu, and Weidong Zhu. "A dynamic adaptive particle swarm optimization and genetic algorithm for different constrained engineering design optimization problems." Advances in Mechanical Engineering 11, no. 3 (2019): 168781401882493. http://dx.doi.org/10.1177/1687814018824930.

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A dynamic adaptive particle swarm optimization and genetic algorithm is presented to solve constrained engineering optimization problems. A dynamic adaptive inertia factor is introduced in the basic particle swarm optimization algorithm to balance the convergence rate and global optima search ability by adaptively adjusting searching velocity during search process. Genetic algorithm–related operators including a selection operator with time-varying selection probability, crossover operator, and n-point random mutation operator are incorporated in the particle swarm optimization algorithm to fu

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Bi, Ya, Anthony Lam, Huiqun Quan, Hui Liu, and Cunfa Wang. "A comprehensively improved particle swarm optimization algotithm to guarantee particle activity." Izvestiya vysshikh uchebnykh zavedenii. Fizika, no. 5 (2021): 94–101. http://dx.doi.org/10.17223/00213411/64/5/94.

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The particle swarm optimization algorithm has the disadvantages, for instance, the convergence viscosity of the algorithm is reduced at the post evolution phase, the optimization search efficiency is reduced, the algorithm is easy to be inserted with local extremum during the calculation of complex problem of high-dimensional multiple extremum, and the convergence thereof is low. As to the disadvantage of the PSO, we proposed a particle swarm optimization of comprehensive improvement strategy, which is a simple particle swarm optimization with dynamic adaptive hybridization of extremum disturb

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Aivaliotis-Apostolopoulos, Panagiotis, and Dimitrios Loukidis. "Swarming genetic algorithm: A nested fully coupled hybrid of genetic algorithm and particle swarm optimization." PLOS ONE 17, no. 9 (2022): e0275094. http://dx.doi.org/10.1371/journal.pone.0275094.

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Particle swarm optimization and genetic algorithms are two classes of popular heuristic algorithms that are frequently used for solving complex multi-dimensional mathematical optimization problems, each one with its one advantages and shortcomings. Particle swarm optimization is known to favor exploitation over exploration, and as a result it often converges rapidly to local optima other than the global optimum. The genetic algorithm has the ability to overcome local extrema throughout the optimization process, but it often suffers from slow convergence rates. This paper proposes a new hybrid

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Yu, Sheng Long, Yu Ming Bo, Zhi Min Chen, and Kai Zhu. "Vehicle Path Planning Method Based on Particle Swarm Optimization Algorithm." Advanced Materials Research 468-471 (February 2012): 2745–48. http://dx.doi.org/10.4028/www.scientific.net/amr.468-471.2745.

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A particle swarm optimization algorithm (PSO) is presented for vehicle path planning in the paper. Particle swarm optimization proposed by Kennedy and Eberhart is derived from the social behavior of the birds foraging. Particle swarm optimization algorithm a kind of swarm-based optimization method.The simulation experiments performed in this study show the better vehicle path planning ability of PSO than that of adaptive genetic algorithm and genetic algorithm. The experimental results show that the vehicle path planning by using PSO algorithm has the least cost and it is indicated that PSO al

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Huang, Xiabao, Zailin Guan, and Lixi Yang. "An effective hybrid algorithm for multi-objective flexible job-shop scheduling problem." Advances in Mechanical Engineering 10, no. 9 (2018): 168781401880144. http://dx.doi.org/10.1177/1687814018801442.

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Genetic algorithm is one of primary algorithms extensively used to address the multi-objective flexible job-shop scheduling problem. However, genetic algorithm converges at a relatively slow speed. By hybridizing genetic algorithm with particle swarm optimization, this article proposes a teaching-and-learning-based hybrid genetic-particle swarm optimization algorithm to address multi-objective flexible job-shop scheduling problem. The proposed algorithm comprises three modules: genetic algorithm, bi-memory learning, and particle swarm optimization. A learning mechanism is incorporated into gen

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Zhao, Jiangbin, Shubin Si, Zhiqiang Cai, Ming Su, and Wei Wang. "Multiobjective optimization of reliability–redundancy allocation problems for serial parallel-series systems based on importance measure." Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability 233, no. 5 (2019): 881–97. http://dx.doi.org/10.1177/1748006x19844785.

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For complex equipment, designers are challenged to reduce the expense while satisfying high requirements of system reliability. This article focuses on the multiobjective reliability–redundancy allocation problem for serial parallel-series systems to balance the conflicts between system reliability and design cost. The multiobjective reliability–redundancy allocation problem model for serial parallel-series systems is established with constraints on system reliability and design cost. An importance measure–based and harmony search–based multiobjective particle swarm optimization algorithm is p

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Gusti, Ahmad Fanshuri Alfarisy, Firdaus Mahmudy Wayan, and Halim Natsir Muhammad. "Optimizing Laying Hen Diet using Multi-swarm Particle Swarm Optimization." TELKOMNIKA Telecommunication, Computing, Electronics and Control 16, no. 4 (2018): 1712–23. https://doi.org/10.12928/TELKOMNIKA.v16i4.7765.

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Formulating animal diet by accounting fluctuating cost, nutrient requirement, balanced amino acids, and maximum composition simultaneously is a difficult and complex task. Manual formulation and Linear Programming encounter difficulty to solve this problem. Furthermore, the complexity of laying hen diet problem is change through ingredient choices. Thus, an advanced technique to enhance formula quality is a vital necessity. This paper proposes the Multi-Swarm Particle Swarm Optimization (MSPSO) to enhance the diversity of particles and prevent premature convergence in PSO. MSPSO work cooperati

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Lenin, Kanagasabai. "Hybridization of Genetic Particle Swarm Optimization Algorithm with Symbiotic Organisms Search Algorithm for Solving Optimal Reactive Power Dispatch Problem." Journal of Applied Science, Engineering, Technology, and Education 3, no. 1 (2020): 12–21. http://dx.doi.org/10.35877/454ri.asci31106.

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In this work Hybridization of Genetic Particle Swarm Optimization Algorithm with Symbiotic Organisms Search Algorithm (HGPSOS) has been done for solving the power dispatch problem. Genetic particle swarm optimization problem has been hybridized with Symbiotic organisms search (SOS) algorithm to solve the problem. Genetic particle swarm optimization algorithm is formed by combining the Particle swarm optimization algorithm (PSO) with genetic algorithm (GA). Symbiotic organisms search algorithm is based on the actions between two different organisms in the ecosystem- mutualism, commensalism and

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Gou, Shuai. "Active Distribution Network Reactive Power Optimization Based on an Improved Genetic Particle Swarm Optimization Algorithm." Journal of Physics: Conference Series 2655, no. 1 (2023): 012035. http://dx.doi.org/10.1088/1742-6596/2655/1/012035.

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Abstract As the distributed generation is gradually connected to the distribution network, it will lead to an increase in node voltage deviation and network loss. The active distribution network reactive power optimization approach based on an improved genetic particle swarm optimization algorithm is proposed in this paper to increase voltage quality and system efficiency. First, a reactive power optimization model for active distribution networks that has the least possible active power loss and voltage variation is developed. Then, the genetic idea is applied to the particle swarm optimizati

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Sun, Tao, and Ming-hai Xu. "A Swarm Optimization Genetic Algorithm Based on Quantum-Behaved Particle Swarm Optimization." Computational Intelligence and Neuroscience 2017 (2017): 1–15. http://dx.doi.org/10.1155/2017/2782679.

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Quantum-behaved particle swarm optimization (QPSO) algorithm is a variant of the traditional particle swarm optimization (PSO). The QPSO that was originally developed for continuous search spaces outperforms the traditional PSO in search ability. This paper analyzes the main factors that impact the search ability of QPSO and converts the particle movement formula to the mutation condition by introducing the rejection region, thus proposing a new binary algorithm, named swarm optimization genetic algorithm (SOGA), because it is more like genetic algorithm (GA) than PSO in form. SOGA has crossov

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Zheng, Qiangang, Haoying Chen, Yong Wang, Haibo Zhang, and Zhongzhi Hu. "Research on hybrid optimization and deep learning modeling method in the performance seeking control." Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering 234, no. 7 (2020): 1340–55. http://dx.doi.org/10.1177/0954410020903151.

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A novel performance seeking control method based on hybrid optimization algorithm and deep learning modeling method is proposed to get a better engine performance. The deep learning modeling method, deep neural network, which has strong representation capability and can deal with big training data, is adopted to establish an on-board engine model. A hybrid optimization algorithm—genetic algorithm particle swarm optimization–feasible sequential quadratic programming—is proposed and applied to performance seeking control. The genetic algorithm particle swarm optimization–feasible sequential quad

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Sulistijono, Indra Adji, and Naoyuki Kubota. "Human Head Tracking Based on Particle Swarm Optimization and Genetic Algorithm." Journal of Advanced Computational Intelligence and Intelligent Informatics 11, no. 6 (2007): 681–87. http://dx.doi.org/10.20965/jaciii.2007.p0681.

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This paper compares particle swarm optimization and a genetic algorithm for perception by a partner robot. The robot requires visual perception to interact with human beings. It should basically extract moving objects using visual perception in interaction with human beings. To reduce computational cost and time consumption, we used differential extraction. We propose human head tracking for a partner robot using particle swarm optimization and a genetic algorithm. Experiments involving two maximum iteration numbers show that particle swarm optimization is more effective in solving this proble

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Hashemi, Seyyed Mohammad, and Iraj Rahmani. "Numerical Comparison of the Performance of Genetic Algorithm and Particle Swarm Optimization in Excavations." Civil Engineering Journal 4, no. 9 (2018): 2186. http://dx.doi.org/10.28991/cej-03091149.

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Today, the back analysis methods are known as reliable and effective approaches for estimating the soil strength parameters in the site of project. The back analysis can be performed by genetic algorithm and particle swarm optimization in the form of an optimization process. In this paper, the back analysis is carried out using genetic algorithm and particle swarm optimization in order to determine the soil strength parameters in an excavation project in Tehran city. The process is automatically accomplished by linking between MATLAB and Abaqus software using Python programming language. To as

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Zhang, Guan Yu, Xiao Ming Wang, Rui Guo, and Guo Qiang Wang. "An Improved Particle Swarm Optimization Algorithm." Applied Mechanics and Materials 394 (September 2013): 505–8. http://dx.doi.org/10.4028/www.scientific.net/amm.394.505.

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This paper presents an improved particle swarm optimization (PSO) algorithm based on genetic algorithm (GA) and Tabu algorithm. The improved PSO algorithm adds the characteristics of genetic, mutation, and tabu search into the standard PSO to help it overcome the weaknesses of falling into the local optimum and avoids the repeat of the optimum path. By contrasting the improved and standard PSO algorithms through testing classic functions, the improved PSO is found to have better global search characteristics.

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Eddine, Ghouraf Djamel, and Naceri Abdellatif. "Efficient Multi-objective Optimizers by Meta-heuristics for Power System Control." WSEAS TRANSACTIONS ON COMPUTERS 21 (December 5, 2022): 316–24. http://dx.doi.org/10.37394/23205.2022.21.38.

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This paper proposes the Meta-heuristics approaches using genetic algorithms (GA) and particle swarm optimization (PSO) for tuning power system stabilizer PSS parameters. In this work we have proposed a multi-objective function based on two objectives: first maximize the stability margin by increasing the damping factors and second minimize the eigenvalues real parts. For the effectiveness function proposed check, we compared it with mono-objective function. The simulation results, by comparative study between genetic algorithms and particle swarm optimizations techniques via multi objective an

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Wu, Yanmin, and Qipeng Song. "Improved Particle Swarm Optimization Algorithm in Power System Network Reconfiguration." Mathematical Problems in Engineering 2021 (March 11, 2021): 1–10. http://dx.doi.org/10.1155/2021/5574501.

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With the rapid development of the social economy, the rapid development of all social circles places higher demands on the electricity industry. As a fundamental industry supporting the salvation of the national economy, society, and human life, the electricity industry will face a significant improvement and the restructuring of the network as an important part of the power system should also be optimised. This paper first introduces the development history of swarm intelligence algorithm and related research work at home and abroad. Secondly, it puts forward the importance of particle swarm

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Li, Rongrong, Linrun Qiu, and Dongbo Zhang. "Research on an Improved Coordinating Method Based on Genetic Algorithms and Particle Swarm Optimization." International Journal of Cognitive Informatics and Natural Intelligence 13, no. 2 (2019): 18–29. http://dx.doi.org/10.4018/ijcini.2019040102.

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In this article, a hierarchical cooperative algorithm based on the genetic algorithm and the particle swarm optimization is proposed that the paper should utilize the global searching ability of genetic algorithm and the fast convergence speed of particle swarm optimization. The proposed algorithm starts from Individual organizational structure of subgroups and takes full advantage of the merits of the particle swarm optimization algorithm and the genetic algorithm (HCGA-PSO). The algorithm uses a layered structure with two layers. The bottom layer is composed of a series of genetic algorithm

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Niu, Ji Gao, Feng Lai Pei, Su Zhou, and Tong Zhang. "Multi-Objective Optimization Study of Energy Management Strategy for Extended-Range Electric Vehicle." Advanced Materials Research 694-697 (May 2013): 2704–9. http://dx.doi.org/10.4028/www.scientific.net/amr.694-697.2704.

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A new method based on genetic-particle swarm hybrid algorithm was presented for parameter optimization of energy management strategy for extended-range electric vehicle (E-REV). Taking a logic threshold control strategy of an E-REV as example, for the aims of minimizing fuel consumption and emissions, a constrained nonlinear programming parameter optimization model was established. Based on this model, genetic algorithm (GA) and particle swarm optimization (PSO) were improved respectively. Further, a genetic-particle swarm hybrid algorithm was put forward and applied to the multi-objective opt

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Chen, Xiang Yang, and Heng Zhen Yan. "Intelligence Optimization Design Model of Deep Cement-Stirring Pile." Applied Mechanics and Materials 44-47 (December 2010): 1505–8. http://dx.doi.org/10.4028/www.scientific.net/amm.44-47.1505.

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Aiming at the phenomenon of the more conservative design of deep cement stirring pile currently, used optimization design theory such as genetic algorithm and particle swarm optimization, taken the cement consumption as the object function, taken replacement rate, water-cement ratio, pile diameter and pile length as the design variables, composite foundation bearing capacity and settlement as restrictive conditions, the optimal design models are established respectively based on genetic algorithm and particle swarm optimization. Case studies have shown that these two established models are eff

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Kangah, Justice Kojo, Justice Kwame Appati, Kwaku F. Darkwah, and Michael Agbo Tettey Soli. "Implementation of an H-PSOGA Optimization Model for Vehicle Routing Problem." International Journal of Applied Metaheuristic Computing 12, no. 3 (2021): 148–62. http://dx.doi.org/10.4018/ijamc.2021070106.

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This work presents an ensemble method which combines both the strengths and weakness of particle swarm optimization (PSO) with genetic algorithm (GA) operators like crossover and mutation to solve the vehicle routing problem. Given that particle swarm optimization and genetic algorithm are both population-based heuristic search evolutionary methods as used in many fields, the standard particle swarm optimization stagnates particles more quickly and converges prematurely to suboptimal solutions which are not guaranteed to be local optimum. Although both PSO and GA are approximation methods to a

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Kamali, Samira. "Portfolio Optimization Using Particle Swarm Optimization And Genetic Algorithm." Journal of Mathematics and Computer Science 10, no. 02 (2014): 85–90. http://dx.doi.org/10.22436/jmcs.010.02.01.

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HU, Gui-wu. "Generalized genetic particle swarm optimization for supply chain optimization." Journal of Computer Applications 28, no. 11 (2009): 2840–43. http://dx.doi.org/10.3724/sp.j.1087.2008.02840.

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Prahara, Murinto, and Erik Ujianto. "Multilevel Thresholding Image Segmentation Based-Logarithm Decreasing Inertia Weight Particle Swarm Optimization." International Journal of Advances in Soft Computing and its Applications 14, no. 3 (2022): 65–77. http://dx.doi.org/10.15849/ijasca.221128.05.

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Abstract The image segmentatation technique that is often used is thresholding. Image segmentation is a process of dividing the image into different regions according to their similar characteristics. This research proposes a multilevel thresholding algorithm using modified particle swarm optimization to solve a segmentation problem. The threshold optimal values are determined by maximizing Otsu’s objective function using optimization technique namely particle swarm optimization based on the logarithmic decreasing inertia weight (LogDIWPSO). The proposed method reduces the computational time t

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Zhang, Fuqiang, and Jingjing Li. "An Improved Particle Swarm Optimization Algorithm for Integrated Scheduling Model in AGV-Served Manufacturing Systems." Journal of Advanced Manufacturing Systems 17, no. 03 (2018): 375–90. http://dx.doi.org/10.1142/s0219686718500221.

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To address the resources optimization problem of AGV-served manufacturing systems driven by multi-varieties and small-batch production orders, a scheduling model integrating machines and automated guided vehicles (AGVs) is proposed. In this model, the makespan of jobs from raw material storage to finished parts storage through multi-stage processes has been used as the objective function, and the utilization ratios of machines and AGVs have been used as the comprehensive evaluation functions. An improved particle swarm optimization algorithm with the characteristics of main particles and neste

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Huang, Mei Geng, and Zhi Qi Ou. "Review of Task Scheduling Algorithm Research in Cloud Computing." Advanced Materials Research 926-930 (May 2014): 3236–39. http://dx.doi.org/10.4028/www.scientific.net/amr.926-930.3236.

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The cloud computing task scheduling field representative algorithms was introduced and analyzed : genetic algorithm, particle swarm optimization, ant colony algorithm. Parallelism and global search solution space is the characteristic of genetic algorithm, genetic iterations difficult to proceed when genetic individuals are very similar; Particle swarm optimization in the initial stage is fast, slow convergence speed in the later stage ; Ant colony algorithm optimization ability is good, slow convergence speed in its first stage; Finally, the summary and prospect the future research direction.

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Patel G C, Manjunath, Prasad Krishna, Mahesh B. Parappagoudar, and Pandu Ranga Vundavilli. "Multi-Objective Optimization of Squeeze Casting Process using Evolutionary Algorithms." International Journal of Swarm Intelligence Research 7, no. 1 (2016): 55–74. http://dx.doi.org/10.4018/ijsir.2016010103.

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The present work focuses on determining optimum squeeze casting process parameters using evolutionary algorithms. Evolutionary algorithms, such as genetic algorithm, particle swarm optimization, and multi objective particle swarm optimization based on crowing distance mechanism, have been used to determine the process variable combinations for the multiple objective functions. In multi-objective optimization, there are no single optimal process variable combination due to conflicting nature of objective functions. Four cases have been considered after assigning different combination of weights

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Shrivastava, Prateek, and Khemraj Deshmukh. "SIMULATION OF PSO BASED APPROACH FOR CMOL CELL ASSIGNMENT PROBLEM." International Journal of Research -GRANTHAALAYAH 3, no. 5 (2015): 1–12. http://dx.doi.org/10.29121/granthaalayah.v3.i5.2015.3009.

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Particle swarm optimization (PSO) approach is used over genetic algorithms (GAS) to solve many of the same kinds of problems. This optimization technique does not suffer, however, from some of GA’s difficulties; interaction in the group enhances rather than detracts from progress toward the solution. Further, a particle swarm system has memory, which the genetic algorithm does not have. In particle swarm optimization, individuals who fly past optima are tugged to return toward them; knowledge of good solutions is retained by all particles. The genetic algorithm works with the concept of chromo

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Prateek, Shrivastava, and Deshmukh Khemraj. "SIMULATION OF PSO BASED APPROACH FOR CMOL CELL ASSIGNMENT PROBLEM." International Journal of Research - GRANTHAALAYAH 3, no. 5 (2017): 1–12. https://doi.org/10.5281/zenodo.883557.

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Particle swarm optimization (PSO) approach is used over genetic algorithms (GAS) to solve many of the same kinds of problems. This optimization technique does not suffer, however, from some of GA’s difficulties; interaction in the group enhances rather than detracts from progress toward the solution. Further, a particle swarm system has memory, which the genetic algorithm does not have. In particle swarm optimization, individuals who fly past optima are tugged to return toward them; knowledge of good solutions is retained by all particles. The genetic algorithm works with the concept of chromo

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Rahmalia, Dinita, Teguh Herlambang, and Thomy Eko Saputro. "Fertilizer Production Planning Optimization Using Particle Swarm Optimization-Genetic Algorithm." Journal of Information Systems Engineering and Business Intelligence 5, no. 2 (2019): 120. http://dx.doi.org/10.20473/jisebi.5.2.120-130.

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Background: The applications of constrained optimization have been developed in many problems. One of them is production planning. Production planning is the important part for controlling the cost spent by the company.Objective: This research identifies about production planning optimization and algorithm to solve it in approaching. Production planning model is linear programming model with constraints : production, worker, and inventory.Methods: In this paper, we use heurisitic Particle Swarm Optimization-Genetic Algorithm (PSOGA) for solving production planning optimization. PSOGA is the al

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Wu, Ang, Juanhua Zhu, Yuli Yang, et al. "Classification of corn kernels grades using image analysis and support vector machine." Advances in Mechanical Engineering 10, no. 12 (2018): 168781401881764. http://dx.doi.org/10.1177/1687814018817642.

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In order to classify the quality of corn kernels in an affordable, convenient, and accurate manner, a method based on image analysis and support vector machine is proposed. A total of 129 corn kernels with Grade A, Grade B, and Grade C are used for the experiments. Six typical characteristic parameters of samples are extracted as the characteristic groups. Four different classifiers are applied and compared: support vector machine-genetic algorithm, support vector machine-particle swarm optimization, support vector machine-grid search optimization, and back-propagation neural networks. Experim

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Kim, Yong Jae, Hyun Joo Kim, and Byung Soo Kim. "Population-Based Search Algorithms for Biopharmaceutical Manufacturing Scheduling Problem with Heterogeneous Parallel Mixed Flowshops." Mathematics 13, no. 3 (2025): 485. https://doi.org/10.3390/math13030485.

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In this paper, we address biopharmaceutical manufacturing scheduling problems with heterogeneous parallel mixed flowshops. The mixed flowshop consists of three stages, one batch process and two continuous processes. The objective function is to minimize the total tardiness. We formulated a mixed-integer linear programming model for the problem to obtain optimal solutions to small-size problems. We present a genetic algorithm and particle swarm optimization, which are used to find efficient solutions for large-size problems. We show that the particle swarm optimization outperforms the genetic a

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Han, Hai Tao, Wan Feng Ji, Yao Qing Zhang, and De Peng Sha. "Comparative Study of Path Planning by Particle Swarm Optimization and Genetic Algorithm." Applied Mechanics and Materials 687-691 (November 2014): 1420–24. http://dx.doi.org/10.4028/www.scientific.net/amm.687-691.1420.

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Two main requirements of the optimization problems are included: one is finding the global minimum, the other is obtaining fast convergence speed. As heuristic algorithm and swarm intelligence algorithm, both particle swarm optimization and genetic algorithm are widely used in vehicle path planning because of their favorable search performance. This paper analyzes the characteristics and the same and different points of two algorithms as well as making simulation experiment under the same operational environment and threat states space. The result shows that particle swarm optimization is supe

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Hachimi, H., Rachid Ellaia, and A. El Hami. "A New Hybrid Genetic Algorithm and Particle Swarm Optimization." Key Engineering Materials 498 (January 2012): 115–25. http://dx.doi.org/10.4028/www.scientific.net/kem.498.115.

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In this paper, we present a new hybrid algorithm which is a combination of a hybrid genetic algorithm and particle swarm optimization. We focus in this research on a hybrid method combining two heuristic optimization techniques, genetic algorithms (GA) and particle swarm optimization (PSO) for the global optimization. Denoted asGA-PSO, this hybrid technique incorporates concepts fromGAandPSOand creates individuals in a new generation not only by crossover and mutation operations as found inGAbut also by mechanisms ofPSO. The performance of the two algorithms has been evaluated using several ex

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Shi, Qin, Chengwang Peng, Yikai Chen, Jie He, Peiqing Li, and Jiajia Chen. "Robust kinematics design of MacPherson suspension based on a double-loop multi-objective particle swarm optimization algorithm." Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering 233, no. 12 (2019): 3263–78. http://dx.doi.org/10.1177/0954407018821556.

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In the traditional design of a vehicle suspension system, mechanical parameters, such as spring stiffness and tire radial stiffness, are determined before optimizing the coordinates of suspension hard-points based on handling and stability. However, the mechanical parameters of vehicle suspension systems vary with repeated changes in ambient temperature and loading conditions. Consequently, the original hard-point coordinates cannot guarantee satisfactory handling and stability after long-term vehicle use. To address this issue, an A0 class vehicle with a MacPherson suspension was modeled in A

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Sun, Erjie, Zhang Xiao, and Yu Tan. "A Fast Path Planning Method of Seedling Tray Replanting Based on Improved Particle Swarm Optimization." Agronomy 13, no. 3 (2023): 853. http://dx.doi.org/10.3390/agronomy13030853.

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In the factory nursery, qualified seedlings can be used to replant unqualified seedlings or missing seedlings in the seedling tray through automatic transplanters. Due to the random positions of unqualified and missing seedlings, the end effector of the automatic replanting machine spends substantial time shuttling between the supply tray and the target tray to complete the replanting task. Therefore, we proposed a fast path planning method based on improved particle swarm optimization and compared it with the fixed sequence method and genetic algorithm in experiments with different replanting

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LIU, JIAN, CHUNYAN WU, XIANGYIN WANG, and DEJIE YU. "AUTOMOTIVE PARTS' LOADING OPTIMIZATION BASED ON IMPROVED QUADRATIC PARTICLE SWARM OPTIMIZATION." International Journal of Humanoid Robotics 07, no. 04 (2010): 699–712. http://dx.doi.org/10.1142/s0219843610002283.

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The present article designed a genetic quadratic particle swarm optimization (GQPSO). Aiming at the low particle diversity at the early searching stage of quadratic particle swarm optimization (QPSO), the method adopts mutation and exchanging and regenerating mechanisms from genetic algorithm so as to avoid premature convergence and improves optimization. Meanwhile, the present article gave a comprehensive consideration to decision elements such as cost, resources, and service in the process of automotive parts' logistics, transportation, and loading; a model of automotive parts' logistics, tr

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Du, Wenli, and Fei Zhang. "Genetic mechanism-enhanced standard particle swarm optimization 2011." Soft Computing 22, no. 21 (2017): 7207–25. http://dx.doi.org/10.1007/s00500-017-2724-2.

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Jabor, Ali Hakem, and Ali Hussein Ali. "Dual Heuristic Feature Selection Based on Genetic Algorithm and Binary Particle Swarm Optimization." JOURNAL OF UNIVERSITY OF BABYLON for Pure and Applied Sciences 27, no. 1 (2019): 171–83. http://dx.doi.org/10.29196/jubpas.v27i1.2106.

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The features selection is one of the data mining tools that used to select the most important features of a given dataset. It contributes to save time and memory during the handling a given dataset. According to these principles, we have proposed features selection method based on mixing two metaheuristic algorithms Binary Particle Swarm Optimization and Genetic Algorithm work individually. The K-Nearest Neighbour (K-NN) is used as an objective function to evaluate the proposed features selection algorithm. The Dual Heuristic Feature Selection based on Genetic Algorithm and Binary Particle Swa

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Eddine, Ghouraf Djamel, Naceri Abdellatif, and Sayeh Abdelkader. "Power System Stabilizer Design using Genetic Algorithms and Particle Swarm Optimization." Control Systems and Optimization Letters 1, no. 1 (2023): 52–57. http://dx.doi.org/10.59247/csol.v1i1.15.

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in this paper, Meta-heuristic techniques using genetic algorithms GA and particle swarm optimization PSO to tuning optimal design of power system stabilizer PSS proposed. This latter have been used for many years to add damping to electromechanical oscillations of power system, Based on this idea we have proposed multiobjective function composed with tow function, first maximize stability margin by increasing the damping factors while minimizing the real parts of the eigenvalues. Simulation results to comparative study between genetic algorithms and particle swarm optimization obtained by our

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Xiong, Zhe, Xiao-Hui Li, Jing-Chang Liang, and Li-Juan Li. "A Multi-Objective Hybrid Algorithm for Optimization of Grid Structures." International Journal of Applied Mechanics 10, no. 01 (2018): 1850009. http://dx.doi.org/10.1142/s1758825118500096.

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In this study, a novel multi-objective hybrid algorithm (MHGH, multi-objective HPSO-GA hybrid algorithm) is developed by crossing the heuristic particle swarm optimization (HPSO) algorithm with a genetic algorithm (GA) based on the concept of Pareto optimality. To demonstrate the effectiveness of the MHGH, the optimizations of four unconstrained mathematical functions and four constrained truss structural problems are tested and compared to the results using several other classic algorithms. The results show that the MHGH improves the convergence rate and precision of the particle swarm optimi

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Qiu, Chang Hua, and Can Wang. "An Immune Particle Swarm Optimization Algorithm for Solving Permutation Flowshop Problem." Key Engineering Materials 419-420 (October 2009): 133–36. http://dx.doi.org/10.4028/www.scientific.net/kem.419-420.133.

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To solve the permutation flowshop problem more effectively, a novel artificial immune particle swarm optimization (PSO) algorithm has been proposed. The new algorithm combined the biology immune system theory with particle swarm algorithm by the following phases. Firstly, the scheduling objective and constrain condition were served as antibodies while solutions was served as antigens. Secondly, the particles were encoded as workpiece processing sequence. Furthermore, a concentration selection strategy was adopted to maintain the particle diversity. Finally, comparing with genetic algorithm and

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Yang, Jinghong. "Traveling Salesman Problem Solving Based on Hybrid Particle Swarm Optimization Algorithm." Advances in Engineering Technology Research 6, no. 1 (2023): 708. http://dx.doi.org/10.56028/aetr.6.1.708.2023.

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Genetic algorithms all the methods used to solve the Traveling Agent (TSP) problem, the use of genetic algorithms on TSP problems is more common than other methods, and it is also possible to achieve global search and obtain the best values. Compared with the traditional genetic algorithm, the particle swarm algorithm is not only easy to be applied, but also can be widely used because of the function of rapid search, but at the same time, the shortcomings of the traditional standard particle swarm algorithm are more prominent, and it is easy to fall into the situation of optimal solution of lo

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Makadia, Jiten, and C. D. Sankhavara. "Optimization of Shell and Tube Heat Exchanger Using Alpha Tuning Elephant Herding Optimization (EHO) Technique." International Journal of Engineering Research in Africa 52 (January 2021): 92–101. http://dx.doi.org/10.4028/www.scientific.net/jera.52.92.

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Swarm Intelligence algorithms like PSO (Particle Swarm Optimization), ACO (Ant Colony Optimization), ABC (Artificial Bee Colony), Glow-worm swarm Optimization, etc. have been utilized by researchers for solving optimization problems. This work presents the application of a novel modified EHO (Elephant Herding Optimization) for cost optimization of shell and tube heat exchanger. A comparison of the results obtained by EHO in two benchmark problems shows that it is superior to those obtained with genetic algorithm and particle swarm optimization. The overall cost reduction is 13.3 % and 9.68% fo

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Dahiya, Tripti, and Deepika Garg. "Reliability optimization using hybrid genetic and particle swarm optimization algorithm." Yugoslav Journal of Operations Research 32, no. 4 (2022): 439–52. http://dx.doi.org/10.2298/yjor220316020d.

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Redundancy-allocation problem i.e. RAP is among the reliability optimization problems which make use of non-linear programming method to improve the reliability of complex system. The objective of this research paper is reliability optimization through the application of Genetic Algorithm i.e. GA and Hybrid Genetic & Particle Swarm Optimization (H-GAPSO) on a RAP. Certain shortcomings have been seen when results are obtained by application of single algorithms. In order to get rid of these shortcomings, HGA-PSO is introduced where attractive properties of GA and PSO are combined. This hybr

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Rozario, Victor Stany Rozario, and Partha Sutradhar. "In-Depth Case Study on Artificial Neural Network Weights Optimization Using Meta-Heuristic and Heuristic Algorithmic Approach." AIUB Journal of Science and Engineering (AJSE) 21, no. 2 (2022): 98–109. http://dx.doi.org/10.53799/ajse.v21i2.379.

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The Meta-heuristic and Heuristic algorithms that have been introduced for deep neural network optimization is in this paper. Artificial Intelligence, and also the most used Deep Learning methods are all growing in popularity these days, thus we need faster optimization strategies for finding the results of future activities. Neural Network Optimization with Particle Swarm Optimization, Backpropagation (BP), Resilient Propagation (Rprop), and Genetic Algorithm (GA) is used for numerical analysis of different datasets and comparing each other to find out which algorithms work better for finding

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