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Journal articles on the topic 'Particle Swarm algorithms'

Author: Grafiati

Published: 28 June 2021

Last updated: 7 February 2022

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1

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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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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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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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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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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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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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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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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9

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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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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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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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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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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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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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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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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Schutte, Jaco F., Byung-Il Koh, Jeffrey A. Reinbolt, Raphael T. Haftka, Alan D. George, and Benjamin J. Fregly. "Evaluation of a Particle Swarm Algorithm For Biomechanical Optimization." Journal of Biomechanical Engineering 127, no. 3 (2005): 465–74. http://dx.doi.org/10.1115/1.1894388.

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Optimization is frequently employed in biomechanics research to solve system identification problems, predict human movement, or estimate muscle or other internal forces that cannot be measured directly. Unfortunately, biomechanical optimization problems often possess multiple local minima, making it difficult to find the best solution. Furthermore, convergence in gradient-based algorithms can be affected by scaling to account for design variables with different length scales or units. In this study we evaluate a recently- developed version of the particle swarm optimization (PSO) algorithm to

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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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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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Song, Ming Li. "A Study of Single-Objective Particle Swarm Optimization and Multi-Objective Particle Swarm Optimization." Applied Mechanics and Materials 543-547 (March 2014): 1635–38. http://dx.doi.org/10.4028/www.scientific.net/amm.543-547.1635.

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The complexity of optimization problems encountered in various modeling algorithms makes a selection of a proper optimization vehicle crucial. Developments in particle swarm algorithm since its origin along with its benefits and drawbacks are mainly discussed as particle swarm optimization provides a simple realization mechanism and high convergence speed. We discuss several developments for single-objective case problem and multi-objective case problem.

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Wang, Haiyan, and Zhiyu Zhou. "A Heuristic Elastic Particle Swarm Optimization Algorithm for Robot Path Planning." Information 10, no. 3 (2019): 99. http://dx.doi.org/10.3390/info10030099.

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Path planning, as the core of navigation control for mobile robots, has become the focus of research in the field of mobile robots. Various path planning algorithms have been recently proposed. In this paper, in view of the advantages and disadvantages of different path planning algorithms, a heuristic elastic particle swarm algorithm is proposed. Using the path planned by the A* algorithm in a large-scale grid for global guidance, the elastic particle swarm optimization algorithm uses a shrinking operation to determine the globally optimal path formed by locally optimal nodes so that the part

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Ma, Zi Rui. "Particle Swarm Optimization Based on Multiobjective Optimization." Applied Mechanics and Materials 263-266 (December 2012): 2146–49. http://dx.doi.org/10.4028/www.scientific.net/amm.263-266.2146.

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PSO will population each individual as the search space without a volume and quality of particle. These particles in the search space at a certain speed flight, the speed according to its own flight experience and the entire population of flight experience dynamic adjustment. We describe the standard PSO, multi-objective optimization and MOPSO. The main focus of this thesis is several PSO algorithms which are introduced in detail and studied. MOPSO algorithm introduced adaptive grid mechanism of the external population, not only to groups of particle on variation, but also to the value scope o

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Hudaib, Amjad A., and Ahmad Kamel AL Hwaitat. "Movement Particle Swarm Optimization Algorithm." Modern Applied Science 12, no. 1 (2017): 148. http://dx.doi.org/10.5539/mas.v12n1p148.

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Particle Swarm Optimization (PSO) ia a will known meta-heuristic that has been used in many applications for solving optimization problems. But it has some problems such as local minima. In this paper proposed a optimization algorithm called Movement Particle Swarm Optimization (MPSO) that enhances the behavior of PSO by using a random movement function to search for more points in the search space. The meta-heuristic has been experimented over 23 benchmark faction compared with state of the art algorithms: Multi-Verse Optimizer (MFO), Sine Cosine Algorithm (SCA), Grey Wolf Optimizer (GWO) and

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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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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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Spears, William M., Derek T. Green, and Diana F. Spears. "Biases in Particle Swarm Optimization." International Journal of Swarm Intelligence Research 1, no. 2 (2010): 34–57. http://dx.doi.org/10.4018/jsir.2010040103.

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The most common versions of particle swarm optimization (PSO) algorithms are rotationally variant. It has also been pointed out that PSO algorithms can concentrate particles along paths parallel to the coordinate axes. In this paper, the authors explicitly connect these two observations by showing that the rotational variance is related to the concentration along lines parallel to the coordinate axes. Based on this explicit connection, the authors create fitness functions that are easy or hard for PSO to solve, depending on the rotation of the function.

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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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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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Alatas, Bilal, Erhan Akin, and A. Bedri Ozer. "Chaos embedded particle swarm optimization algorithms." Chaos, Solitons & Fractals 40, no. 4 (2009): 1715–34. http://dx.doi.org/10.1016/j.chaos.2007.09.063.

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Zhou, Fei Hong, and Zi Zhen Liao. "A Particle Swarm Optimization Algorithm." Applied Mechanics and Materials 303-306 (February 2013): 1369–72. http://dx.doi.org/10.4028/www.scientific.net/amm.303-306.1369.

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The basic and improved algorithms of PSO focus on how to effectively search the optimal solution in the solution space using one of the particle swarm. However, the particles are always chasing the global optimal point and such points currently found on their way of search, rapidly leading their speed down to zero and hence being restrained in the local minimum. Consequently, the convergence or early maturity of particles exists. The improved PSO is based on the enlightenment of BP neural network while the improvement is similar to smooth the weight through low-pass filter. The test of classic

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Wei, Xiao-peng, Jian-xia Zhang, Dong-sheng Zhou, and Qiang Zhang. "Multiswarm Particle Swarm Optimization with Transfer of the Best Particle." Computational Intelligence and Neuroscience 2015 (2015): 1–9. http://dx.doi.org/10.1155/2015/904713.

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We propose an improved algorithm, for a multiswarm particle swarm optimization with transfer of the best particle called BMPSO. In the proposed algorithm, we introduce parasitism into the standard particle swarm algorithm (PSO) in order to balance exploration and exploitation, as well as enhancing the capacity for global search to solve nonlinear optimization problems. First, the best particle guides other particles to prevent them from being trapped by local optima. We provide a detailed description of BMPSO. We also present a diversity analysis of the proposed BMPSO, which is explained based

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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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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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Zhang, Chun Yan, and Wei Chen. "Quantum-Behaved Particle Swarm Optimization Dynamic Clustering Algorithm." Advanced Materials Research 694-697 (May 2013): 2757–60. http://dx.doi.org/10.4028/www.scientific.net/amr.694-697.2757.

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This paper proposed a revised quantum-behaved particle swarm optimization algorithm utilizing comprehensive learning strategy to prevent the universal tendency of premature convergence, based on which introduced a novel data clustering algorithm as well. The optimal number of cluster could be automatically obtained by this novel clustering algorithm because a new special coding method for particles was used. Compared with another two dynamic clustering algorithms on five testing data sets, the proposed dynamic clustering algorithm based on the comprehensive learning strategy has the best perfo

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Ahmad, Yasir, Mohib Ullah, Rafiullah Khan, et al. "SiFSO: Fish Swarm Optimization-Based Technique for Efficient Community Detection in Complex Networks." Complexity 2020 (December 12, 2020): 1–9. http://dx.doi.org/10.1155/2020/6695032.

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Efficient community detection in a complex network is considered an interesting issue due to its vast applications in many prevailing areas such as biology, chemistry, linguistics, social sciences, and others. There are several algorithms available for network community detection. This study proposed the Sigmoid Fish Swarm Optimization (SiFSO) algorithm to discover efficient network communities. Our proposed algorithm uses the sigmoid function for various fish moves in a swarm, including Prey, Follow, Swarm, and Free Move, for better movement and community detection. The proposed SiFSO algorit

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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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Yong, Wang, Wang Tao, Zhang Cheng-Zhi, and Huang Hua-Juan. "A New Stochastic Optimization Approach — Dolphin Swarm Optimization Algorithm." International Journal of Computational Intelligence and Applications 15, no. 02 (2016): 1650011. http://dx.doi.org/10.1142/s1469026816500115.

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A novel nature-inspired swarm intelligence (SI) optimization is proposed called dolphin swarm optimization algorithm (DSOA), which is based on mimicking the mechanism of dolphins in detecting, chasing after, and preying on swarms of sardines to perform optimization. In order to test the performance, the DSOA is evaluated against the corresponding results of three existing well-known SI optimization algorithms, namely, particle swarm optimization (PSO), bat algorithm (BA), and artificial bee colony (ABC), in the terms of the ability to find the global optimum of a range of the popular benchmark

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Freitas Vaz, António Ismael de, and Edite Manuela da Graça Pinto Fernandes. "OPTIMIZATION OF NONLINEAR CONSTRAINED PARTICLE SWARM." Technological and Economic Development of Economy 12, no. 1 (2006): 30–36. http://dx.doi.org/10.3846/13928619.2006.9637719.

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We propose an algorithm based on the particle swarm paradigm (PSP) to address nonlinear constrained optimization problems. While some algorithms based on PSP have already been proposed in this context, the equality constraints have been posing some difficulties. The proposed algorithm is based on the relaxation of the dominance concept introduced in the multiobjective optimization. This concept is used to select the best particle position and the best ever particle swarm position. We propose also a stopping criterion for the algorithm and present numerical results with some problems collected

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Yong, Dong, Wu Chuansheng, and Guo Haimin. "Particle Swarm Optimization Algorithm with Adaptive Chaos Perturbation." Cybernetics and Information Technologies 15, no. 6 (2015): 70–80. http://dx.doi.org/10.1515/cait-2015-0068.

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Abstract Most of the existing chaotic particle swarm optimization algorithms use logistic chaotic mapping. However, the chaotic sequence which is generated by the logistic chaotic mapping is not uniform enough. As a solution to this defect, this paper introduces the Anderson chaotic mapping to the chaotic particle swarm optimization, using it to initialize the position and velocity of the particle swarm. It self-adaptively controls the portion of particles to undergo chaos update through a change of the fitness variance. The numerical simulation results show that the convergence and global sea

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

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

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Passaro, Alessandro, and Antonina Starita. "Particle Swarm Optimization for Multimodal Functions: A Clustering Approach." Journal of Artificial Evolution and Applications 2008 (April 24, 2008): 1–15. http://dx.doi.org/10.1155/2008/482032.

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The particle swarm optimization (PSO) algorithm is designed to find a single optimal solution and needs some modifications to be able to locate multiple optima on a multimodal function. In parallel with evolutionary computation algorithms, these modifications can be grouped in the framework of niching. In this work, we present a new approach to niching in PSO based on clustering particles to identify niches. The neighborhood structure, on which particles rely for communication, is exploited together with the niche information to locate multiple optima in parallel. Our approach was implemented

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Lenin, K. "DIMENSIONED PARTICLE SWARM OPTIMIZATION FOR REACTIVE POWER OPTIMIZATION PROBLEM." International Journal of Research -GRANTHAALAYAH 6, no. 4 (2018): 281–90. http://dx.doi.org/10.29121/granthaalayah.v6.i4.2018.1663.

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This paper present’s Dimensioned Particle Swarm Optimization (DPSO) algorithm for solving Reactive power optimization (RPO) problem. Dimensioned extension is introduced to particles in the particle swarm optimization (PSO) model in order to overcome premature convergence in interactive optimization. In the performance of basic PSO often flattens out with a loss of diversity in the search space as resulting in local optimal solution. Proposed algorithm has been tested in standard IEEE 57 test bus system and compared to other standard algorithms. Simulation results reveal about the best performa

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Boursianis, Achilles D., Maria S. Papadopoulou, Marco Salucci, et al. "Emerging Swarm Intelligence Algorithms and Their Applications in Antenna Design: The GWO, WOA, and SSA Optimizers." Applied Sciences 11, no. 18 (2021): 8330. http://dx.doi.org/10.3390/app11188330.

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Swarm Intelligence (SI) Algorithms imitate the collective behavior of various swarms or groups in nature. In this work, three representative examples of SI algorithms have been selected and thoroughly described, namely the Grey Wolf Optimizer (GWO), the Whale Optimization Algorithm (WOA), and the Salp Swarm Algorithm (SSA). Firstly, the selected SI algorithms are reviewed in the literature, specifically for optimization problems in antenna design. Secondly, a comparative study is performed against widely known test functions. Thirdly, such SI algorithms are applied to the synthesis of linear a

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Xiao, Bin, and Zhao Hui Li. "An Improved Hybrid Discrete Particle Swarm Optimization Algorithm to Solve the TSP Problem." Applied Mechanics and Materials 130-134 (October 2011): 3589–94. http://dx.doi.org/10.4028/www.scientific.net/amm.130-134.3589.

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Through investigating the issue of solving the TSP problem by discrete particle swarm optimization algorithm, this study finds a new discrete particle swarm optimization algorithm (NDPSO), which is easy to combine with other algorithm and has fast convergence and high accuracy, by introducing the thought of the greedy algorithm and GA algorithm and refining the discrete particle swarm optimization algorithm. And then the study expands NDPSO by Simulated Annealing algorithm and proposes a hybrid discrete particle swarm optimization algorithm (HDPSO). At last, the experiments prove that these tw

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Wang, Lei, and Yongqiang Liu. "Application of Simulated Annealing Particle Swarm Optimization Based on Correlation in Parameter Identification of Induction Motor." Mathematical Problems in Engineering 2018 (July 8, 2018): 1–9. http://dx.doi.org/10.1155/2018/1869232.

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The strengths and weaknesses of correlation algorithm, simulated annealing algorithm, and particle swarm optimization algorithm are studied in this paper. A hybrid optimization algorithm is proposed by drawing upon the three algorithms, and the specific application processes are given. To extract the current fundamental signal, the correlation algorithm is used. To identify the motor dynamic parameter, the filtered stator current signal is simulated using simulated annealing particle swarm algorithm. The simulated annealing particle swarm optimization algorithm effectively incorporates the glo

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Liu, Ruochen, Chenlin Ma, Wenping Ma, and Yangyang Li. "A Multipopulation PSO Based Memetic Algorithm for Permutation Flow Shop Scheduling." Scientific World Journal 2013 (2013): 1–11. http://dx.doi.org/10.1155/2013/387194.

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The permutation flow shop scheduling problem (PFSSP) is part of production scheduling, which belongs to the hardest combinatorial optimization problem. In this paper, a multipopulation particle swarm optimization (PSO) based memetic algorithm (MPSOMA) is proposed in this paper. In the proposed algorithm, the whole particle swarm population is divided into three subpopulations in which each particle evolves itself by the standard PSO and then updates each subpopulation by using different local search schemes such as variable neighborhood search (VNS) and individual improvement scheme (IIS). The

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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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Yang, Wusi, Li Chen, Yi Wang, and Maosheng Zhang. "Multi/Many-Objective Particle Swarm Optimization Algorithm Based on Competition Mechanism." Computational Intelligence and Neuroscience 2020 (February 20, 2020): 1–26. http://dx.doi.org/10.1155/2020/5132803.

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The recently proposed multiobjective particle swarm optimization algorithm based on competition mechanism algorithm cannot effectively deal with many-objective optimization problems, which is characterized by relatively poor convergence and diversity, and long computing runtime. In this paper, a novel multi/many-objective particle swarm optimization algorithm based on competition mechanism is proposed, which maintains population diversity by the maximum and minimum angle between ordinary and extreme individuals. And the recently proposed θ-dominance is adopted to further enhance the performanc

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Mohapatra, Prabhujit, Kedar Nath Das, Santanu Roy, Ram Kumar, and Nilanjan Dey. "A Novel Multi-Objective Competitive Swarm Optimization Algorithm." International Journal of Applied Metaheuristic Computing 11, no. 4 (2020): 114–29. http://dx.doi.org/10.4018/ijamc.2020100106.

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In this article, a new algorithm, namely the multi-objective competitive swarm optimizer (MOCSO), is introduced to handle multi-objective problems. The algorithm has been principally motivated from the competitive swarm optimizer (CSO) and the NSGA-II algorithm. In MOCSO, a pair wise competitive scenario is presented to achieve the dominance relationship between two particles in the population. In each pair wise competition, the particle that dominates the other particle is considered the winner and the other is consigned as the loser. The loser particles learn from the respective winner parti

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