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Journal articles on the topic 'Particle Swarm algorithms'

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Published: 28 June 2021

Last updated: 29 July 2025

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1

Lenin, K. "CROWDING DISTANCE BASED PARTICLE SWARM OPTIMIZATION ALGORITHM FOR SOLVING OPTIMAL REACTIVE POWER DISPATCH PROBLEM." International Journal of Research -GRANTHAALAYAH 6, no. 6 (2018): 226–37. http://dx.doi.org/10.29121/granthaalayah.v6.i6.2018.1369.

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In this paper, Crowding Distance based Particle Swarm Optimization (CDPSO) algorithm has been proposed to solve the optimal reactive power dispatch problem. Particle Swarm Optimization (PSO) is swarm intelligence-based exploration and optimization algorithm which is used to solve global optimization problems. In PSO, the population is referred as a swarm and the individuals are called particles. Like other evolutionary algorithms, PSO performs searches using a population of individuals that are updated from iteration to iteration. The crowding distance is introduced as the index to judge the d

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Dr.K., Lenin. "CROWDING DISTANCE BASED PARTICLE SWARM OPTIMIZATION ALGORITHM FOR SOLVING OPTIMAL RECATIVE POWER DISPATCH PROBLEM." International Journal of Research - Granthaalayah 6, no. 6 (2018): 226–37. https://doi.org/10.5281/zenodo.1305360.

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In this paper, Crowding Distance based Particle Swarm Optimization (CDPSO) algorithm has been proposed to solve the optimal reactive power dispatch problem. Particle Swarm Optimization (PSO) is swarm intelligence-based exploration and optimization algorithm which is used to solve global optimization problems. In PSO, the population is referred as a swarm and the individuals are called particles. Like other evolutionary algorithms, PSO performs searches using a population of individuals that are updated from iteration to iteration. The crowding distance is introduced as the index to judge the d

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3

Zhang, Fan, and Zhongsheng Chen. "A Novel Reinforcement Learning-Based Particle Swarm Optimization Algorithm for Better Symmetry between Convergence Speed and Diversity." Symmetry 16, no. 10 (2024): 1290. http://dx.doi.org/10.3390/sym16101290.

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This paper introduces a novel Particle Swarm Optimization (RLPSO) algorithm based on reinforcement learning, embodying a fundamental symmetry between global and local search processes. This symmetry aims at addressing the trade-off issue between convergence speed and diversity in traditional algorithms. Traditional Particle Swarm Optimization (PSO) algorithms often struggle to maintain good convergence speed and particle diversity when solving multi-modal function problems. To tackle this challenge, we propose a new algorithm that incorporates the principles of reinforcement learning, enabling

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Wang, H. D., C. N. Zhang, H. Zhang, Y. C. Wei, and X. L. Guan. "A Quantum Particle Swarm Optimization Algorithm Based on Aggregation Perturbation." Applied Science and Innovative Research 7, no. 4 (2023): p21. http://dx.doi.org/10.22158/asir.v7n4p21.

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A quantum particle swarm hybrid optimization algorithm based on aggregation disturbance is proposed for inventory cost control. This algorithm integrates the K-means algorithm on the basis of traditional particle swarm optimization, recalculates the clustering center, initializes stagnant particles, and solves the problem of particle aggregation. Introducing chaos mechanism into the algorithm, changing the position of particles, enhancing their activity, and improving the algorithm's global optimization ability. At the same time, define the aggregation disturbance factor, determine the current

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Liu, Hong Ying. "Utilize Improved Particle Swarm to Predict Traffic Flow." Advanced Materials Research 756-759 (September 2013): 3744–48. http://dx.doi.org/10.4028/www.scientific.net/amr.756-759.3744.

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Presented an improved particle swarm optimization algorithm, introduced a crossover operation for the particle location, interfered the particles speed, made inert particles escape the local optimum points, enhanced PSO algorithm's ability to break away from local extreme point. Utilized improved algorithms to train the RBF neural network models, predict short-time traffic flow of a region intelligent traffic control. Simulation and test results showed that, the improved algorithm can effetely forecast short-time traffic flow of the regional intelligent transportation control, forecasting effe

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Xie, Zixuan, Xueyu Huang, and Wenwen Liu. "Subpopulation Particle Swarm Optimization with a Hybrid Mutation Strategy." Computational Intelligence and Neuroscience 2022 (February 23, 2022): 1–19. http://dx.doi.org/10.1155/2022/9599417.

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With the large-scale optimization problems in the real world becoming more and more complex, they also require different optimization algorithms to keep pace with the times. Particle swarm optimization algorithm is a good tool that has been proved to deal with various optimization problems. Conventional particle swarm optimization algorithms learn from two particles, namely, the best position of the current particle and the best position of all particles. This particle swarm optimization algorithm is simple to implement, simple, and easy to understand, but it has a fatal defect. It is hard to

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Tang, Kezong, and Chengjian Meng. "Particle Swarm Optimization Algorithm Using Velocity Pausing and Adaptive Strategy." Symmetry 16, no. 6 (2024): 661. http://dx.doi.org/10.3390/sym16060661.

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Particle swarm optimization (PSO) as a swarm intelligence-based optimization algorithm has been widely applied to solve various real-world optimization problems. However, traditional PSO algorithms encounter issues such as premature convergence and an imbalance between global exploration and local exploitation capabilities when dealing with complex optimization tasks. To address these shortcomings, an enhanced PSO algorithm incorporating velocity pausing and adaptive strategies is proposed. By leveraging the search characteristics of velocity pausing and the terminal replacement mechanism, the

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Kong, Fanrong, Jianhui Jiang, and Yan Huang. "An Adaptive Multi-Swarm Competition Particle Swarm Optimizer for Large-Scale Optimization." Mathematics 7, no. 6 (2019): 521. http://dx.doi.org/10.3390/math7060521.

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As a powerful tool in optimization, particle swarm optimizers have been widely applied to many different optimization areas and drawn much attention. However, for large-scale optimization problems, the algorithms exhibit poor ability to pursue satisfactory results due to the lack of ability in diversity maintenance. In this paper, an adaptive multi-swarm particle swarm optimizer is proposed, which adaptively divides a swarm into several sub-swarms and a competition mechanism is employed to select exemplars. In this way, on the one hand, the diversity of exemplars increases, which helps the swa

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Lu, Senxiang, Jinhai Liu, Jing Wu, and Xuewei Fu. "A Fast Globally Convergent Particle Swarm Optimization for Defect Profile Inversion Using MFL Detector." Machines 10, no. 11 (2022): 1091. http://dx.doi.org/10.3390/machines10111091.

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For the problem of defect inversion in magnetic flux leakage technology, a fast, globally convergent particle swarm optimization algorithm based on the finite-element forward model is introduced as an inverse iterative algorithm in this paper. Two aspects of the traditional particle swarm optimization algorithm have been improved: self-adaptive inertia weight and speed updating strategy. For the inertia weight, it can be adaptively adjusted according to the particle position. The speed update strategy mainly uses the best experience positions of other particles in a randomly selected populatio

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Yan, Zheping, Chao Deng, Benyin Li, and Jiajia Zhou. "Novel Particle Swarm Optimization and Its Application in Calibrating the Underwater Transponder Coordinates." Mathematical Problems in Engineering 2014 (2014): 1–12. http://dx.doi.org/10.1155/2014/672412.

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A novel improved particle swarm algorithm named competition particle swarm optimization (CPSO) is proposed to calibrate the Underwater Transponder coordinates. To improve the performance of the algorithm, TVAC algorithm is introduced into CPSO to present anextension competition particle swarm optimization(ECPSO). The proposed method is tested with a set of 10 standard optimization benchmark problems and the results are compared with those obtained through existing PSO algorithms,basic particle swarm optimization(BPSO),linear decreasing inertia weight particle swarm optimization(LWPSO),exponent

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Yao, Wenting, and Yongjun Ding. "Smart City Landscape Design Based on Improved Particle Swarm Optimization Algorithm." Complexity 2020 (December 1, 2020): 1–10. http://dx.doi.org/10.1155/2020/6693411.

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Aiming at the shortcomings of standard particle swarm optimization (PSO) algorithms that easily fall into local optimum, this paper proposes an optimization algorithm (LTQPSO) that improves quantum behavioral particle swarms. Aiming at the problem of premature convergence of the particle swarm algorithm, the evolution speed of individual particles and the population dispersion are used to dynamically adjust the inertia weights to make them adaptive and controllable, thereby avoiding premature convergence. At the same time, the natural selection method is introduced into the traditional positio

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Yang, Meilan, Yanmin Liu, and Jie Yang. "A Hybrid Multi-Objective Particle Swarm Optimization with Central Control Strategy." Computational Intelligence and Neuroscience 2022 (March 9, 2022): 1–23. http://dx.doi.org/10.1155/2022/1522096.

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In recent years, researchers have solved the multi-objective optimization problem by making various improvements to the multi-objective particle swarm optimization algorithm. However, we propose a hybrid multi-objective particle swarm optimization (CCHMOPSO) with a central control strategy. In this algorithm, a disturbance strategy based on boundary fluctuations is first used for the updated new particles and nondominant particles. To prevent the population from falling into a local extremum, some particles are disturbed. Then, when the external archive capacity reaches the extreme value, we u

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Lenin, K. "TAILORED PARTICLE SWARM OPTIMIZATION ALGORITHM FOR SOLVING OPTIMAL REACTIVE POWER PROBLEM." International Journal of Research -GRANTHAALAYAH 5, no. 12 (2020): 246–55. http://dx.doi.org/10.29121/granthaalayah.v5.i12.2017.500.

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This paper presents Tailored Particle Swarm Optimization (TPSO) algorithm for solving optimal reactive power problem. Particle Swarm optimization algorithm based on Membrane Computing is proposed to solve the problem. Tailored Particle Swarm Optimization (TPSO) algorithm designed with the framework and rules of a cell-like P systems, and particle swarm optimization with the neighbourhood search. In order to evaluate the efficiency of the proposed algorithm, it has been tested on standard IEEE 118 & practical 191 bus test systems and compared to other specified algorithms. Simulation result

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Dr.K.Lenin. "TAILORED PARTICLE SWARM OPTIMIZATION ALGORITHM FOR SOLVING OPTIMAL REACTIVE POWER PROBLEM." International Journal of Research - Granthaalayah 5, no. 12 (2017): 246–55. https://doi.org/10.5281/zenodo.1134569.

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This paper presents Tailored Particle Swarm Optimization (TPSO) algorithm for solving optimal reactive power problem. Particle Swarm optimization algorithm based on Membrane Computing is proposed to solve the problem. Tailored Particle Swarm Optimization (TPSO) algorithm designed with the framework and rules of a cell-like P systems, and particle swarm optimization with the neighbourhood search.  In order to evaluate the efficiency of the proposed algorithm, it has been tested on standard IEEE 118 & practical 191 bus test systems and compared to other specified algorithms. Simulation

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15

Wei-Yan Chang, Wei-Yan Chang, Prathibha Soma Wei-Yan Chang, Huan Chen Prathibha Soma, Hsuan Chang Huan Chen, and Chun-Wei Tsai Hsuan Chang. "A Hybrid Firefly with Dynamic Multi-swarm Particle Swarm Optimization for WSN Deployment." 網際網路技術學刊 24, no. 4 (2023): 825–36. http://dx.doi.org/10.53106/160792642023072404001.

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<p>Enhancing the coverage area of the sensing range with the limiting resource is a critical problem in the wireless sensor network (WSN). Mobile sensors are patched coverage holes and they also have limited energy to move in large distances. Several recent studies indicated the metaheuristic algorithms can find an acceptable deployed solution in a reasonable time, especially the PSO-based algorithm. However, the speeds of convergence of most PSO-based algorithms are too fast which will lead to the premature problem to degrade the quality of deployed performance in WSN. A hybrid metaheur

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Zhao, Jing Ying, Hai Guo, and Xiao Niu Li. "Research on Algorithm Optimization of Hidden Units Data Centre of RBF Neural Network." Advanced Materials Research 831 (December 2013): 486–89. http://dx.doi.org/10.4028/www.scientific.net/amr.831.486.

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Common algorithms of selecting hidden unit data center in RBF neural networks were first discussed in this essay, i.e. k-means algorithm, subtractive clustering algorithm and orthogonal least squares. Meanwhile, a hybrid algorithm mixed of k-means algorithm and particle swarm optimization algorithm was put forward. The algorithm used the position of the particles in particle swarm optimization algorithm to help deal with the defects of local clusters resulted from k-means algorithm and to make optimization with the optimal fitness of k-means particle swarm with the aim to make the final optima

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17

Yu, Chengcheng, and Laijun Zhao. "Multi-Objective Particle Swarm Optimization Algorithm based on Position Vector Offset." International Journal of Mechanical and Electrical Engineering 2, no. 2 (2024): 131–35. http://dx.doi.org/10.62051/ijmee.v2n2.15.

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In response to the issue that particle swarm optimization algorithms tend to fall into local optima when dealing with multi-objective optimization tasks, a multi-objective optimization algorithm based on particle swarm is proposed. This algorithm is based on the relationship between the position vectors of particles, changing the selection and movement strategies of particles to find the true Pareto front. Firstly, two additional position vectors are added around the iterating particles to enhance their search capability; then, a non-dominated vector archive is established to record the non-do

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18

Fan, Shu-Kai S., and Chih-Hung Jen. "An Enhanced Partial Search to Particle Swarm Optimization for Unconstrained Optimization." Mathematics 7, no. 4 (2019): 357. http://dx.doi.org/10.3390/math7040357.

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Particle swarm optimization (PSO) is a population-based optimization technique that has been applied extensively to a wide range of engineering problems. This paper proposes a variation of the original PSO algorithm for unconstrained optimization, dubbed the enhanced partial search particle swarm optimizer (EPS-PSO), using the idea of cooperative multiple swarms in an attempt to improve the convergence and efficiency of the original PSO algorithm. The cooperative searching strategy is particularly devised to prevent the particles from being trapped into the local optimal solutions and tries to

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19

Weikert, Dominik, Sebastian Mai, and Sanaz Mostaghim. "Particle Swarm Contour Search Algorithm." Entropy 22, no. 4 (2020): 407. http://dx.doi.org/10.3390/e22040407.

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In this article, we present a new algorithm called Particle Swarm Contour Search (PSCS)—a Particle Swarm Optimisation inspired algorithm to find object contours in 2D environments. Currently, most contour-finding algorithms are based on image processing and require a complete overview of the search space in which the contour is to be found. However, for real-world applications this would require a complete knowledge about the search space, which may not be always feasible or possible. The proposed algorithm removes this requirement and is only based on the local information of the particles to

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20

Ahmad Shaban, Awaz, Jayson A. Dela Fuente, Merdin Shamal Salih, and Resen Ismail Ali. "Review of Swarm Intelligence for Solving Symmetric Traveling Salesman Problem." Qubahan Academic Journal 3, no. 2 (2023): 10–27. http://dx.doi.org/10.48161/qaj.v3n2a141.

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Swarm Intelligence algorithms are computational intelligence algorithms inspired from the collective behavior of real swarms such as ant colony, fish school, bee colony, bat swarm, and other swarms in the nature. Swarm Intelligence algorithms are used to obtain the optimal solution for NP-Hard problems that are strongly believed that their optimal solution cannot be found in an optimal bounded time. Travels Salesman Problem (TSP) is an NP-Hard problem in which a salesman wants to visit all cities and return to the start city in an optimal time. In this article we are applying most efficient he

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Hacibeyoglu, Mehmet, and Mohammed H. Ibrahim. "A Novel Multimean Particle Swarm Optimization Algorithm for Nonlinear Continuous Optimization: Application to Feed-Forward Neural Network Training." Scientific Programming 2018 (July 4, 2018): 1–9. http://dx.doi.org/10.1155/2018/1435810.

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Multilayer feed-forward artificial neural networks are one of the most frequently used data mining methods for classification, recognition, and prediction problems. The classification accuracy of a multilayer feed-forward artificial neural networks is proportional to training. A well-trained multilayer feed-forward artificial neural networks can predict the class value of an unseen sample correctly if provided with the optimum weights. Determining the optimum weights is a nonlinear continuous optimization problem that can be solved with metaheuristic algorithms. In this paper, we propose a nov

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Baktybekov, K. "PARTICLE SWARM OPTIMIZATION WITH INDIVIDUALLY BIASED PARTICLES FOR RELIABLE AND ROBUST MAXIMUM POWER POINT TRACKING UNDER PARTIAL SHADING CONDITIONS." Eurasian Physical Technical Journal 17, no. 2 (2020): 128–37. http://dx.doi.org/10.31489/2020no2/128-137.

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Efficient power control techniques are an integral part of photovoltaic system design. One of the means of managing power delivery is regulating the duty cycle of the DC to DC converter by various algorithms to operate only at points where power is maximum power point. Search has to be done as fast as possible to minimize power loss, especially under dynamically changing irradiance. The challenge of the task is the nonlinear behavior of the PV system under partial shading conditions. Depending on the size and structure of the photovoltaic panels, PSC creates an immense amount of possible P-V c

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Mengxia, Li, Liao Ruiquan, and Dong Yong. "The Particle Swarm Optimization Algorithm with Adaptive Chaos Perturbation." International Journal of Computers Communications & Control 11, no. 6 (2016): 804. http://dx.doi.org/10.15837/ijccc.2016.6.2525.

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Aiming at the two characteristics of premature convergence of particle swarm optimization that the particle velocity approaches 0 and particle swarm congregate, this paper learns from the annealing function of the simulated annealing algorithm and adaptively and dynamically adjusts inertia weights according to the velocity information of particles to avoid approaching 0 untimely. This paper uses the good uniformity of Anderson chaotic mapping and performs chaos perturbation to part of particles based on the information of variance of the population’s fitness to avoid the untimely aggregation o

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Xu, Xinliang, and Fu Yan. "Random walk autonomous groups of particles for particle swarm optimization." Journal of Intelligent & Fuzzy Systems 42, no. 3 (2022): 1519–45. http://dx.doi.org/10.3233/jifs-210867.

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Autonomous groups of particles swarm optimization (AGPSO), inspired by individual diversity in biological swarms such as insects or birds, is a modified particle swarm optimization (PSO) variant. The AGPSO method is simple to understand and easy to implement on a computer. It has achieved an impressive performance on high-dimensional optimization tasks. However, AGPSO also struggles with premature convergence, low solution accuracy and easily falls into local optimum solutions. To overcome these drawbacks, random-walk autonomous group particle swarm optimization (RW-AGPSO) is proposed. In the

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Nie, Shu Zhi, Yan Hua Zhong, and Ming Hu. "Short-Time Traffic Flow Prediction Method Based on Universal Organic Computing Architecture." Advanced Materials Research 756-759 (September 2013): 2785–89. http://dx.doi.org/10.4028/www.scientific.net/amr.756-759.2785.

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Designed a DNA-based genetic algorithm under the universal architecture of organic computing, combined particle swarm optimization algorithm, introduced a crossover operation for the particle location, can interfere with the particles speed, make inert particles escape the local optimum points, enhanced PSO algorithm's ability to get rid of local extreme point. Utilized improved algorithms to train the RBF neural network models, predict short-time traffic flow of a region intelligent traffic control. Simulation and error analysis of experimental results showed that, the designed algorithms can

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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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Liu, Hongbo, and Ajith Abraham. "An Hybrid Fuzzy Variable Neighborhood Particle Swarm Optimization Algorithm for Solving Quadratic Assignment Problems." JUCS - Journal of Universal Computer Science 13, no. (9) (2007): 1309–31. https://doi.org/10.3217/jucs-013-09-1309.

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Recently, Particle Swarm Optimization (PSO) algorithm has exhibited good performance across a wide range of application problems. A quick review of the literature reveals that research for solving the Quadratic Assignment Problem (QAP) using PSO approach has not much been investigated. In this paper, we design a hybrid meta-heuristic fuzzy scheme, called as variable neighborhood fuzzy particle swarm algorithm (VNPSO), based on fuzzy particle swarm optimization and variable neighborhood search to solve the QAP. In the hybrid fuzzy scheme, the representations of the position and velocity of the

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Xi, Maolong, Xiaojun Wu, Xinyi Sheng, Jun Sun, and Wenbo Xu. "Improved quantum-behaved particle swarm optimization with local search strategy." Journal of Algorithms & Computational Technology 11, no. 1 (2016): 3–12. http://dx.doi.org/10.1177/1748301816654020.

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Quantum-behaved particle swarm optimization, which was motivated by analysis of particle swarm optimization and quantum system, has shown compared performance in finding the optimal solutions for many optimization problems to other evolutionary algorithms. To address the problem of premature, a local search strategy is proposed to improve the performance of quantum-behaved particle swarm optimization. In proposed local search strategy, a super particle is presented which is a collection body of randomly selected particles’ dimension information in the swarm. The selected probability of particl

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Chen, Shuai, and Zhe Kan. "Image Reconstruction Algorithm for Electrical Capacitance Tomography Based on Constrained Particle Swarm Optimization." Journal of Physics: Conference Series 2404, no. 1 (2022): 012058. http://dx.doi.org/10.1088/1742-6596/2404/1/012058.

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Abstract When the particle swarm is active, the robustness of the system is quite great, which is very useful for solving ill-conditioned problems like picture reconstruction. In the reconstructed image, however, the large number of pixels results in the large dimension of particles, making it harder for particles to attain the ideal solution during the optimization process. A constraint condition is applied to the position of particles to solve this problem. The image reconstruction algorithm regularized by Tikhonov is used as the particle position reference to constrain the particles to sear

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Qian, Yu Xia, K. Dong, and X. N. Zhang. "Two New Parallel Algorithms Based on QPSO." Applied Mechanics and Materials 743 (March 2015): 325–32. http://dx.doi.org/10.4028/www.scientific.net/amm.743.325.

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Based on the analysis of classical particle swarm optimization (PSO) algorithm, we adopted Sun’s theory that has the behavior of quantum particle swarm optimization (QPSO) algorithm, by analyzing the algorithm natural parallelism and combined with parallel computer high-speed parallelism, we put forward a new parallel with the behavior of quantum particle swarm optimization (PQPSO) algorithm. On this basis, introduced the island model, relative to the fine-grained has two quantum behavior of particle swarm,m optimization algorithm, the proposed two kinds of coarse-grained parallel based on mul

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Li, Zheng Bo. "Learning-Based Multi-Directional Adaptive PSO." Applied Mechanics and Materials 321-324 (June 2013): 2183–86. http://dx.doi.org/10.4028/www.scientific.net/amm.321-324.2183.

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Particle Swarm Optimization (PSO) is a swarm intelligence algorithm to achieve through competition and collaboration between the particles in the complex search space to find the global optimum. Basic PSO algorithm evolutionary late convergence speed is slow and easy to fall into the shortcomings of local minima, this paper presents a multi-learning particle swarm optimization algorithm, the algorithm particle at the same time to follow their own to find the optimal solution, random optimal solution and the optimal solution for the whole group of other particles with dimensions velocity update

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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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CHEN, LEI, and HAI-LIN LIU. "A REGION DECOMPOSITION-BASED MULTI-OBJECTIVE PARTICLE SWARM OPTIMIZATION ALGORITHM." International Journal of Pattern Recognition and Artificial Intelligence 28, no. 08 (2014): 1459009. http://dx.doi.org/10.1142/s0218001414590095.

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In this paper, a novel multi-objective particle swarm optimization algorithm based on MOEA/D-M2M decomposition strategy (MOPSO-M2M) is proposed. MOPSO-M2M can decompose the objective space into a number of subregions and then search all the subregions using respective sub-swarms simultaneously. The M2M decomposition strategy has two very desirable properties with regard to MOPSO. First, it facilitates the determination of the global best (gbest) for each sub-swarm. A new global attraction strategy based on M2M decomposition framework is proposed to guide the flight of particles by setting an a

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Yu, Xiaolu. "Application of Improved Particle Swarm Optimization Algorithm in Logistics Energy-Saving Picking Information Network." Wireless Communications and Mobile Computing 2022 (September 19, 2022): 1–8. http://dx.doi.org/10.1155/2022/6411285.

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In order to solve the logistics optimization problem, an application method of the improved particle swarm optimization algorithm in logistics energy-saving pickup information network is proposed. Firstly, a mathematical model of logistics cycle picking information scheduling optimization is established, logistics and picking paths are encoded as particles, and the optimal logistics cycle picking optimization scheme is found through the cooperation between particles. Secondly, the deficiencies of the particle swarm optimization algorithm are improved accordingly. In order to test the performan

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Behera, Mandakini, Archana Sarangi, Debahuti Mishra, et al. "Automatic Data Clustering by Hybrid Enhanced Firefly and Particle Swarm Optimization Algorithms." Mathematics 10, no. 19 (2022): 3532. http://dx.doi.org/10.3390/math10193532.

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Data clustering is a process of arranging similar data in different groups based on certain characteristics and properties, and each group is considered as a cluster. In the last decades, several nature-inspired optimization algorithms proved to be efficient for several computing problems. Firefly algorithm is one of the nature-inspired metaheuristic optimization algorithms regarded as an optimization tool for many optimization issues in many different areas such as clustering. To overcome the issues of velocity, the firefly algorithm can be integrated with the popular particle swarm optimizat

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Ajeil, Fatin Hassan, Ibraheem Kasim Ibraheem, Ahmad Taher Azar, and Amjad J. Humaidi. "Autonomous navigation and obstacle avoidance of an omnidirectional mobile robot using swarm optimization and sensors deployment." International Journal of Advanced Robotic Systems 17, no. 3 (2020): 172988142092949. http://dx.doi.org/10.1177/1729881420929498.

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The present work deals with the design of intelligent path planning algorithms for a mobile robot in static and dynamic environments based on swarm intelligence optimization. Two modifications are suggested to improve the searching process of the standard bat algorithm with the result of two novel algorithms. The first algorithm is a Modified Frequency Bat algorithm, and the second is a hybridization between the Particle Swarm Optimization with the Modified Frequency Bat algorithm, namely, the Hybrid Particle Swarm Optimization-Modified Frequency Bat algorithm. Both Modified Frequency Bat and

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Shaikh, Tayyab Ahmed, Syed Sajjad Hussain Rizvi, and Muhammad Rizwan Tanweer. "Constrained self regulating particle swarm optimization." Bulletin of Electrical Engineering and Informatics 11, no. 2 (2022): 955–64. http://dx.doi.org/10.11591/eei.v11i2.3564.

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Self regulating particle swarm optimization (SRPSO) is a variant of particle swarm optimization (PSO) which has proved to be a very efficient algorithm for unconstrained optimization compared with other evolutionary algorithms (EAs) and utilized recently by the researchers for solving real-world problems. However, SRPSO has not been evaluated and analyzed for constrained optimization. In this work, SRPSO has been evaluated exhaustively for constrained optimization using the 24 constrained benchmark problems by coupling it with four efficient constraint handling techniques (CHTs). The results o

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Tayyab, Ahmed Shaikh, Sajjad Hussain Rizvi Syed, and Rizwan Tanweer Muhammad. "Constrained self regulating particle swarm optimization." Bulletin of Electrical Engineering and Informatics 11, no. 2 (2022): 955~964. https://doi.org/10.11591/eei.v11i2.3564.

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Self regulating particle swarm optimization (SRPSO) is a variant of particle swarm optimization (PSO) which has proved to be a very efficient algorithm for unconstrained optimization compared with other evolutionary algorithms (EAs) and utilized recently by the researchers for solving real-world problems. However, SRPSO has not been evaluated and analyzed for constrained optimization. In this work, SRPSO has been evaluated exhaustively for constrained optimization using the 24 constrained benchmark problems by coupling it with four efficient constraint handling techniques (CHTs). The results o

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Yamanaka, Yoshikazu, and Katsutoshi Yoshida. "Simple gravitational particle swarm algorithm for multimodal optimization problems." PLOS ONE 16, no. 3 (2021): e0248470. http://dx.doi.org/10.1371/journal.pone.0248470.

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In real world situations, decision makers prefer to have multiple optimal solutions before making a final decision. Aiming to help the decision makers even if they are non-experts in optimization algorithms, this study proposes a new and simple multimodal optimization (MMO) algorithm called the gravitational particle swarm algorithm (GPSA). Our GPSA is developed based on the concept of “particle clustering in the absence of clustering procedures”. Specifically, it simply replaces the global feedback term in classical particle swarm optimization (PSO) with an inverse-square gravitational force

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Liang, Jianhui, Lifang Wang, and Miao Ma. "An Adaptive Dual-Population Collaborative Chicken Swarm Optimization Algorithm for High-Dimensional Optimization." Biomimetics 8, no. 2 (2023): 210. http://dx.doi.org/10.3390/biomimetics8020210.

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With the development of science and technology, many optimization problems in real life have developed into high-dimensional optimization problems. The meta-heuristic optimization algorithm is regarded as an effective method to solve high-dimensional optimization problems. However, considering that traditional meta-heuristic optimization algorithms generally have problems such as low solution accuracy and slow convergence speed when solving high-dimensional optimization problems, an adaptive dual-population collaborative chicken swarm optimization (ADPCCSO) algorithm is proposed in this paper,

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Zou, Kangge, Yanmin Liu, Shihua Wang, Nana Li, and Yaowei Wu. "A Multiobjective Particle Swarm Optimization Algorithm Based on Grid Technique and Multistrategy." Journal of Mathematics 2021 (December 7, 2021): 1–17. http://dx.doi.org/10.1155/2021/1626457.

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When faced with complex optimization problems with multiple objectives and multiple variables, many multiobjective particle swarm algorithms are prone to premature convergence. To enhance the convergence and diversity of the multiobjective particle swarm algorithm, a multiobjective particle swarm optimization algorithm based on the grid technique and multistrategy (GTMSMOPSO) is proposed. The algorithm randomly uses one of two different evaluation index strategies (convergence evaluation index and distribution evaluation index) combined with the grid technique to enhance the diversity and conv

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Lee, Wen-Shing, Wen-Hsin Lin, Chin-Chi Cheng, and Chien-Yu Lin. "Optimal Chiller Loading by Team Particle Swarm Algorithm for Reducing Energy Consumption." Energies 14, no. 21 (2021): 7066. http://dx.doi.org/10.3390/en14217066.

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Energy saving is an important issue for multiple-chiller systems. Optimal chiller loading (OCL) in multiple-chiller systems has been investigated with many optimization algorithms to save energy. Particle swarm optimization (PSO) algorithm has been successful in solving this problem in some cases, but not in all. This study innovatively added a team evolution to the original particle swarm optimization algorithm, called team particle swarm optimization (TPSO). The TPSO enhances the effectiveness of original particle swarm optimization to better solve the OCL problem. The TPSO algorithm is comp

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Song, Yanqing, and Genran Hou. "Research on Optimization of Project Time-Cost-Quality Based on Particle Swarm Optimization." International Journal of Information Systems and Supply Chain Management 12, no. 2 (2019): 76–88. http://dx.doi.org/10.4018/ijisscm.2019040106.

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In order to make proper time-cost-quality decisions for projects, an improved particle swarm optimization algorithm is applied. First, the optimal model of project time-cost-quality is constructed considering all factors. Second, the basic theory of particle swarm algorithms is summarized, and the improved particle swarm algorithm is put forward based on vector principle, and then the rotational base technology is introduced into the improved particle swarm algorithm to construct a multiple objective optimization algorithm. Finally, the simulation analysis is carried out using a project as exa

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von Eschwege, Daniel, and Andries Engelbrecht. "Soft Actor-Critic Approach to Self-Adaptive Particle Swarm Optimisation." Mathematics 12, no. 22 (2024): 3481. http://dx.doi.org/10.3390/math12223481.

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Particle swarm optimisation (PSO) is a swarm intelligence algorithm that finds candidate solutions by iteratively updating the positions of particles in a swarm. The decentralised optimisation methodology of PSO is ideally suited to problems with multiple local minima and deceptive fitness landscapes, where traditional gradient-based algorithms fail. PSO performance depends on the use of a suitable control parameter (CP) configuration, which governs the trade-off between exploration and exploitation in the swarm. CPs that ensure good performance are problem-dependent. Unfortunately, CPs tuning

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Stanovov, Vladimir, Shakhnaz Akhmedova, Aleksei Vakhnin, Evgenii Sopov, Eugene Semenkin, and Michael Affenzeller. "Improving the Quantum Multi-Swarm Optimization with Adaptive Differential Evolution for Dynamic Environments." Algorithms 15, no. 5 (2022): 154. http://dx.doi.org/10.3390/a15050154.

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In this study, the modification of the quantum multi-swarm optimization algorithm is proposed for dynamic optimization problems. The modification implies using the search operators from differential evolution algorithm with a certain probability within particle swarm optimization to improve the algorithm’s search capabilities in dynamically changing environments. For algorithm testing, the Generalized Moving Peaks Benchmark was used. The experiments were performed for four benchmark settings, and the sensitivity analysis to the main parameters of algorithms is performed. It is shown that apply

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Xiang Xu, Xiang Xu, 李儀 Xiang Xu, and Yi-Fan Wang Yi Li. "Particle Swarm Optimization with Long and Short Term Memory in Feature Selection." 電腦學刊 33, no. 5 (2022): 121–33. http://dx.doi.org/10.53106/199115992022103305011.

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<p>Taking each iteration of Particle swarm optimization (PSO) algorithm as a time node, the change of population in PSO algorithm can be regarded as a time series model. Particle population learns and evolves in multiple time nodes, which can be regarded as a dependent behavior on leader particles. In the traditional particle swarm optimization algorithm, this dependence behavior is independent of time, and its consideration standard is only the fitness value of particles. We deeply study the leadership mechanism of PSO algorithm in order to find a more robust leadership mechanism and im

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Zhou, Xin, Shangbo Zhou, Yuxiao Han, and Shufang Zhu. "Lévy flight-based inverse adaptive comprehensive learning particle swarm optimization." Mathematical Biosciences and Engineering 19, no. 5 (2022): 5241–68. http://dx.doi.org/10.3934/mbe.2022246.

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<abstract><p>In the traditional particle swarm optimization algorithm, the particles always choose to learn from the well-behaved particles in the population during the population iteration. Nevertheless, according to the principles of particle swarm optimization, we know that the motion of each particle has an impact on other individuals, and even poorly behaved particles can provide valuable information. Based on this consideration, we propose Lévy flight-based inverse adaptive comprehensive learning particle swarm optimization, called LFIACL-PSO. In the LFIACL-PSO algorithm, Fir

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Ababneh, Jehad. "Greedy particle swarm and biogeography-based optimization algorithm." International Journal of Intelligent Computing and Cybernetics 8, no. 1 (2015): 28–49. http://dx.doi.org/10.1108/ijicc-01-2014-0003.

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Purpose – The purpose of this paper is to propose an algorithm that combines the particle swarm optimization (PSO) with the biogeography-based optimization (BBO) algorithm. Design/methodology/approach – The BBO and the PSO algorithms are jointly used in to order to combine the advantages of both algorithms. The efficiency of the proposed algorithm is tested using some selected standard benchmark functions. The performance of the proposed algorithm is compared with that of the differential evolutionary (DE), genetic algorithm (GA), PSO, BBO, blended BBO and hybrid BBO-DE algorithms. Findings –

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Fernandes, Carlos M., Nuno Fachada, Juan-Julián Merelo, and Agostinho C. Rosa. "Steady state particle swarm." PeerJ Computer Science 5 (August 26, 2019): e202. http://dx.doi.org/10.7717/peerj-cs.202.

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This paper investigates the performance and scalability of a new update strategy for the particle swarm optimization (PSO) algorithm. The strategy is inspired by the Bak–Sneppen model of co-evolution between interacting species, which is basically a network of fitness values (representing species) that change over time according to a simple rule: the least fit species and its neighbors are iteratively replaced with random values. Following these guidelines, a steady state and dynamic update strategy for PSO algorithms is proposed: only the least fit particle and its neighbors are updated and e

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Dioşan, Laura, and Mihai Oltean. "What Else Is the Evolution of PSO Telling Us?" Journal of Artificial Evolution and Applications 2008 (January 28, 2008): 1–12. http://dx.doi.org/10.1155/2008/289564.

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Evolutionary algorithms (EAs) can be used in order to design particle swarm optimization (PSO) algorithms that work, in some cases, considerably better than the human-designed ones. By analyzing the evolutionary process of designing PSO algorithms, we can identify different swarm phenomena (such as patterns or rules) that can give us deep insights about the swarm behavior. The rules that have been observed can help us design better PSO algorithms for optimization. We investigate and analyze swarm phenomena by looking into the process of evolving PSO algorithms. Several test problems have been

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