Relevant bibliographies by topics / Genetic Particle Swarm Optimization

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Genetic Particle Swarm Optimization'

Author: Grafiati
Published: 3 June 2025
Last updated: 31 July 2025

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Journal articles on the topic "Genetic Particle Swarm Optimization"

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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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Dissertations / Theses on the topic "Genetic Particle Swarm Optimization"

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Morcos, Karim M. "Genetic network parameter estimation using single and multi-objective particle swarm optimization." Thesis, Kansas State University, 2011. http://hdl.handle.net/2097/9207.

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Master of Science<br>Department of Electrical and Computer Engineering<br>Sanjoy Das<br>Stephen M. Welch<br>Multi-objective optimization problems deal with finding a set of candidate optimal solutions to be presented to the decision maker. In industry, this could be the problem of finding alternative car designs given the usually conflicting objectives of performance, safety, environmental friendliness, ease of maintenance, price among others. Despite the significance of this problem, most of the non-evolutionary algorithms which are widely used cannot find a set of diverse and nearly optima
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Muthuswamy, Shanthi. "Discrete particle swarm optimization algorithms for orienteering and team orienteering problems." Diss., Online access via UMI:, 2009.

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Franz, Wayne. "Multi-population PSO-GA hybrid techniques: integration, topologies, and parallel composition." Springer, 2013. http://hdl.handle.net/1993/23842.

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Recent work in metaheuristic algorithms has shown that solution quality may be improved by composing algorithms with orthogonal characteristics. In this thesis, I study multi-population particle swarm optimization (MPSO) and genetic algorithm (GA) hybrid strategies. I begin by investigating the behaviour of MPSO with crossover, mutation, swapping, and all three, and show that the latter is able to solve the most difficult benchmark functions. Because GAs converge slowly and MPSO provides a large degree of parallelism, I also develop several parallel hybrid algorithms. A composite approach exec
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Sheehan, Shane P. "Spacecraft Trajectory Optimization Suite (STOPS): Optimization of Low-Thrust Interplanetary Spacecraft Trajectories Using Modern Optimization Techniques." DigitalCommons@CalPoly, 2017. https://digitalcommons.calpoly.edu/theses/1901.

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The work presented here is a continuation of Spacecraft Trajectory Optimization Suite (STOpS), a master’s thesis written by Timothy Fitzgerald at California Polytechnic State University, San Luis Obispo. Low-thrust spacecraft engines are becoming much more common due to their high efficiency, especially for interplanetary trajectories. The version of STOpS presented here optimizes low-thrust trajectories using the Island Model Paradigm with three stochastic evolutionary algorithms: the genetic algorithm, differential evolution, and particle swarm optimization. While the algorithms used here we
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Espinosa, Edgard. "Design Optimization of Submerged Jet Nozzles for Enhanced Mixing." FIU Digital Commons, 2011. http://digitalcommons.fiu.edu/etd/501.

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The purpose of this thesis was to identify the optimal design parameters for a jet nozzle which obtains a local maximum shear stress while maximizing the average shear stress on the floor of a fluid filled system. This research examined how geometric parameters of a jet nozzle, such as the nozzle's angle, height, and orifice, influence the shear stress created on the bottom surface of a tank. Simulations were run using a Computational Fluid Dynamics (CFD) software package to determine shear stress values for a parameterized geometric domain including the jet nozzle. A response surface was crea
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Grobler, Jacomine. "Particle swarm optimization and differential evolution for multi-objective multiple machine scheduling." Diss., Pretoria : [s.n.], 2009. http://upetd.up.ac.za/thesis/available/etd-05062009-164124/.

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Hadavi, Hamid. "Isometry Registration Among Deformable Objects, A Quantum Optimization with Genetic Operator." Thèse, Université d'Ottawa / University of Ottawa, 2013. http://hdl.handle.net/10393/24286.

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Non-rigid shapes are generally known as objects whose three dimensional geometry may deform by internal and/or external forces. Deformable shapes are all around us, ranging from protein molecules, to natural objects such as the trees in the forest or the fruits in our gardens, and even human bodies. Two deformable shapes may be related by isometry, which means their intrinsic geometries are preserved, even though their extrinsic geometries are dissimilar. An important problem in the analysis of the deformable shapes is to identify the three-dimensional correspondence between two isometric shap
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Zuniga, Virgilio. "Bio-inspired optimization algorithms for smart antennas." Thesis, University of Edinburgh, 2011. http://hdl.handle.net/1842/5766.

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This thesis studies the effectiveness of bio-inspired optimization algorithms in controlling adaptive antenna arrays. Smart antennas are able to automatically extract the desired signal from interferer signals and external noise. The angular pattern depends on the number of antenna elements, their geometrical arrangement, and their relative amplitude and phases. In the present work different antenna geometries are tested and compared when their array weights are optimized by different techniques. First, the Genetic Algorithm and Particle Swarm Optimization algorithms are used to find the best
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Al-Obaidi, Mohanad. "ENAMS : energy optimization algorithm for mobile wireless sensor networks using evolutionary computation and swarm intelligence." Thesis, De Montfort University, 2010. http://hdl.handle.net/2086/5187.

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Although traditionally Wireless Sensor Network (WSNs) have been regarded as static sensor arrays used mainly for environmental monitoring, recently, its applications have undergone a paradigm shift from static to more dynamic environments, where nodes are attached to moving objects, people or animals. Applications that use WSNs in motion are broad, ranging from transport and logistics to animal monitoring, health care and military. These application domains have a number of characteristics that challenge the algorithmic design of WSNs. Firstly, mobility has a negative effect on the quality of
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Liu, Fang. "Nature inspired computational intelligence for financial contagion modelling." Thesis, Brunel University, 2014. http://bura.brunel.ac.uk/handle/2438/8208.

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Financial contagion refers to a scenario in which small shocks, which initially affect only a few financial institutions or a particular region of the economy, spread to the rest of the financial sector and other countries whose economies were previously healthy. This resembles the “transmission” of a medical disease. Financial contagion happens both at domestic level and international level. At domestic level, usually the failure of a domestic bank or financial intermediary triggers transmission by defaulting on inter-bank liabilities, selling assets in a fire sale, and undermining confidence
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Books on the topic "Genetic Particle Swarm Optimization"

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Lazinica, Aleksandar. Particle swarm optimization. InTech, 2009.

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Mercangöz, Burcu Adıgüzel, ed. Applying Particle Swarm Optimization. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-70281-6.

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Couceiro, Micael, and Pedram Ghamisi. Fractional Order Darwinian Particle Swarm Optimization. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-19635-0.

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Mikki, Said M., and Ahmed A. Kishk. Particle Swarm Optimization: A Physics-Based Approach. Springer International Publishing, 2008. http://dx.doi.org/10.1007/978-3-031-01704-9.

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Olsson, Andrea E. Particle swarm optimization: Theory, techniques, and applications. Nova Science Publishers, 2010.

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1974-, Parsopoulos Konstantinos E., and Vrahatis Michael N. 1955-, eds. Particle swarm optimization and intelligence: Advances and applications. Information Science Reference, 2010.

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Parsopoulos, Konstantinos E. Particle swarm optimization and intelligence: Advances and applications. Information Science Reference, 2010.

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Kiranyaz, Serkan, Turker Ince, and Moncef Gabbouj. Multidimensional Particle Swarm Optimization for Machine Learning and Pattern Recognition. Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-642-37846-1.

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Choi-Hong, Lai, and Wu Xiao-Jun, eds. Particle swarm optimisation: Classical and quantum perspectives. CRC Press, 2011.

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Siarry, Patrick, Nicolas Monmarché, Nicolas Monmarché, and Frederic Guinand. Artificial ants: From collective intelligence to real-life optimization and beyond. ISTE, 2010.

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Book chapters on the topic "Genetic Particle Swarm Optimization"

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Kim, D. H., Ajith Abraham, and K. Hirota. "Hybrid Genetic: Particle Swarm Optimization Algorithm." In Hybrid Evolutionary Algorithms. Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-73297-6_7.

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Wang, Lianguo, Yi Hong, Fuqing Zhao, and Dongmei Yu. "A Multiagent Genetic Particle Swarm Optimization." In Advances in Computation and Intelligence. Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-92137-0_72.

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Zhang, Liping, Huanjun Yu, and Shangxu Hu. "A New Approach to Improve Particle Swarm Optimization." In Genetic and Evolutionary Computation — GECCO 2003. Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/3-540-45105-6_12.

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Qi, Feng, Yinghong Ma, Xiyu Liu, and Guangyong Ji. "A Hybrid Genetic Programming with Particle Swarm Optimization." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-38715-9_2.

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Ren, Da, Yi Cai, and Han Huang. "Genetic Learning Particle Swarm Optimization with Diverse Selection." In Intelligent Computing Methodologies. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-95957-3_83.

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Loebl, Jaroslav, and Viera Rozinajová. "Continuous Cartesian Genetic Programming with Particle Swarm Optimization." In Advances in Intelligent Systems and Computing. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-16660-1_96.

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Eberhart, Russell C., and Yuhui Shi. "Comparison between genetic algorithms and particle swarm optimization." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/bfb0040812.

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Dou, Chunhong, and Jinshan Lin. "Improved Particle Swarm Optimization Based on Genetic Algorithm." In Advances in Intelligent and Soft Computing. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-25349-2_20.

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Borowska, Bożena. "Genetic Learning Particle Swarm Optimization with Interlaced Ring Topology." In Lecture Notes in Computer Science. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-50426-7_11.

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Wang, Jiahai. "Genetic Particle Swarm Optimization Based on Estimation of Distribution." In Bio-Inspired Computational Intelligence and Applications. Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-74769-7_32.

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Conference papers on the topic "Genetic Particle Swarm Optimization"

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Engelbrecht, Andries. "Particle Swarm Optimization." In GECCO '15: Genetic and Evolutionary Computation Conference. ACM, 2015. http://dx.doi.org/10.1145/2739482.2756564.

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Engelbrecht, Andries. "Particle swarm optimization." In GECCO '14: Genetic and Evolutionary Computation Conference. ACM, 2014. http://dx.doi.org/10.1145/2598394.2605342.

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Engelbrecht, AP, and CW Cleghorn. "Particle swarm optimization." In GECCO '18: Genetic and Evolutionary Computation Conference. ACM, 2018. http://dx.doi.org/10.1145/3205651.3207877.

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Cleghorn, Christopher W., and Andries P. Engelbrecht. "Fitness-distance-ratio particle swarm optimization." In GECCO '17: Genetic and Evolutionary Computation Conference. ACM, 2017. http://dx.doi.org/10.1145/3071178.3071256.

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Peng, Meng-Qi, Yue-Jiao Gong, Jing-Jing Li, and Ying-Biao Lin. "Multi-swarm particle swarm optimization with multiple learning strategies." In GECCO '14: Genetic and Evolutionary Computation Conference. ACM, 2014. http://dx.doi.org/10.1145/2598394.2598418.

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Kumazawa, Tsutomu, Munehiro Takimoto, and Yasushi Kambayashi. "A safety checking algorithm with multi-swarm particle swarm optimization." In GECCO '22: Genetic and Evolutionary Computation Conference. ACM, 2022. http://dx.doi.org/10.1145/3520304.3528918.

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Abadlia, Houda, Nadia Smairi, and Khaled Ghedira. "Particle swarm optimization based on island models." In GECCO '17: Genetic and Evolutionary Computation Conference. ACM, 2017. http://dx.doi.org/10.1145/3067695.3076068.

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Strasser, Shane, Rollie Goodman, John Sheppard, and Stephyn Butcher. "A New Discrete Particle Swarm Optimization Algorithm." In GECCO '16: Genetic and Evolutionary Computation Conference. ACM, 2016. http://dx.doi.org/10.1145/2908812.2908935.

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Xiao, Heng, and Toshiharu Hatanaka. "Hybrid swarm of particle swarm with firefly for complex function optimization." In GECCO '18: Genetic and Evolutionary Computation Conference. ACM, 2018. http://dx.doi.org/10.1145/3205651.3208776.

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Liu, Fang, and Bo Peng. "Immune-Particle Swarm Optimization Beats Genetic Algorithms." In 2010 Second Global Congress on Intelligent Systems (GCIS). IEEE, 2010. http://dx.doi.org/10.1109/gcis.2010.14.

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Reports on the topic "Genetic Particle Swarm Optimization"

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Vtipil, Sharon, and John G. Warner. Earth Observing Satellite Orbit Design Via Particle Swarm Optimization. Defense Technical Information Center, 2014. http://dx.doi.org/10.21236/ada625084.

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Sonugür, Güray, Celal Onur Gçkçe, Yavuz Bahadır Koca, and Şevket Semih Inci. Particle Swarm Optimization Based Optimal PID Controller for Quadcopters. "Prof. Marin Drinov" Publishing House of Bulgarian Academy of Sciences, 2021. http://dx.doi.org/10.7546/crabs.2021.12.11.

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Gökçe, Barış, Yavuz Bahadır Koca, Yılmaz Aslan, and Celal Onur Gökçe. Particle Swarm Optimization-based Optimal PID Control of an Agricultural Mobile Robot. "Prof. Marin Drinov" Publishing House of Bulgarian Academy of Sciences, 2021. http://dx.doi.org/10.7546/crabs.2021.04.12.

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Davis, Jeremy, Amy Bednar, and Christopher Goodin. Optimizing maximally stable extremal regions (MSER) parameters using the particle swarm optimization algorithm. Engineer Research and Development Center (U.S.), 2019. http://dx.doi.org/10.21079/11681/34160.

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Styling Parameter Optimization of the Type C Recreational Vehicle Air Drag. SAE International, 2021. http://dx.doi.org/10.4271/2021-01-5094.

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Recreational vehicles have a lot of potential consumers in China, especially the type C recreational vehicle is popular among consumers due to its advantages, prompting an increase in the production and sales volumes. The type C vehicle usually has a higher air drag than the common commercial vehicles due to its unique appearance. It can be reduced by optimizing the structural parameters, thus the energy consumed by the vehicle can be decreased. The external flow field of a recreational vehicle is analyzed by establishing its computational fluid dynamic (CFD) model. The characteristic of the R
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RESEARCH ON DATA-DRIVEN INTELLIGENT DESIGN METHOD FOR ENERGY DISSIPATOR OF FLEXIBLE PROTECTION SYSTEMS. The Hong Kong Institute of Steel Construction, 2024. https://doi.org/10.18057/ijasc.2024.20.4.6.

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The brake ring, an essential buffer and energy dissipator within flexible protection systems for mitigating dynamic impacts from rockfall collapses, presents notable design challenges due to its significant deformation and strain characteristics. This study introduces a highly efficient and precise neural network model tailored for the design of brake rings, utilizing BP neural networks in conjunction with Particle Swarm Optimization (PSO) algorithms. The paper studies the key geometric parameters, including ring diameter, tube diameter, wall thickness, and aluminum sleeve length, with perform
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