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Particle swarm optimization  An Overview
 SWARM INTELL
, 2007
"... Particle swarm optimization (PSO) has undergone many changes since its introduction in 1995. As researchers have learned about the technique, they have derived new versions, developed new applications, and published theoretical studies of the effects of the various parameters and aspects of the algo ..."
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Particle swarm optimization (PSO) has undergone many changes since its introduction in 1995. As researchers have learned about the technique, they have derived new versions, developed new applications, and published theoretical studies of the effects of the various parameters and aspects of the algorithm. This paper comprises a snapshot of particle swarming from the authors’ perspective, including variations in the algorithm, current and ongoing research, applications and open problems.
Particle swarm optimization of a modified bernstein polynomial for conformal array excitation synthesis,” in 2004
 IEEE Antennas Propagation Soc. Int. Symp. Dig
, 2005
"... Abstract—As various enabling technologies advance, conformal phased arrays are finding more numerous applications. Because a conformal array is curved, new far field pattern behaviors emerge and many of the traditional linear and planar phased array synthesis methods are not valid. This paper start ..."
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Abstract—As various enabling technologies advance, conformal phased arrays are finding more numerous applications. Because a conformal array is curved, new far field pattern behaviors emerge and many of the traditional linear and planar phased array synthesis methods are not valid. This paper starts by reviewing the equations for the far field of a curved phased array, and provides a generalized definition of aperture efficiency appropriate for conformal arrays. A modified Bernstein polynomial, defined with just five parameters, is introduced which provides a flexible method to specify a variety of smooth unimodal amplitude distributions that are shown to give good sidelobe levels and aperture efficiencies. By using particle swarm optimization of the modified Bernstein polynomial parameters constrained to provide a specified aperture efficiency, a family of aperture distributions and corresponding far field patterns is produced that allows aperture efficiency to be traded for sidelobe level. Index Terms—Antenna arrays, antenna radiation patterns, conformal antennas, optimization methods. I.
A SelfLearning Particle Swarm Optimizer for Global Optimization Problems
, 2011
"... Particle swarm optimization (PSO) has been shown as an effective tool for solving global optimization problems. So far, most PSO algorithms use a single learning pattern for all particles, which means all particles in a swarm use the same strategy. This monotonic learning pattern may cause the lack ..."
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Cited by 6 (0 self)
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Particle swarm optimization (PSO) has been shown as an effective tool for solving global optimization problems. So far, most PSO algorithms use a single learning pattern for all particles, which means all particles in a swarm use the same strategy. This monotonic learning pattern may cause the lack of intelligence for a particular particle, which makes it unable to deal with different complex situations. This paper presents a novel algorithm, called selflearning particle swarm optimizer (SLPSO), for global optimization problems. In SLPSO, each particle has a set of four strategies to cope with different situations in the search space. The cooperation of the four strategies is implemented by an adaptive learning framework at the individual level, which can enable a particle to choose the optimal strategy according to its own local fitness landscape. The experimental study on a set of 45 test functions and two realworld problems show that SLPSO has a superior performance in comparison with several other peer algorithms.
Alternative Fuels Mixture in Cement Industry Kilns Employing Particle Swarm Optimization Algorithm
"... Most of the works accomplished in the optimization area in the cement industry are addressed to solve problems just considering only one variable, forgetting that it includes too many variables and they act at the same time. Among the main variables it can be mentioned the quality of the final produ ..."
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Most of the works accomplished in the optimization area in the cement industry are addressed to solve problems just considering only one variable, forgetting that it includes too many variables and they act at the same time. Among the main variables it can be mentioned the quality of the final product, the environmental ones, the costs along the process and the reduction of the fossil fuels (primary) employed through the use of alternative fuels (secondary), among others. The present work intends to build a mathematical model using optimization tools seeking to improve the cement production process foreseeing what can happen with the clinker and the emissions when the industrial residues coprocessing technology is used as alternative or secondary fuel. In the optimization process a new approach called Particle Swarm Optimization (PSO) is employed, which is based on the Cauchy and Gauss distribution considering several process restrictions such as the specific fuel consumption, the cement quality and the environmental impact. The results obtained with PSO were precise and promising and they were compared with the classical Sequential Quadratic Programming (SQP). It was also possible to evaluate the levels of primary fuels substitution through the alternative or secondary ones.
Article
"... A new species of the bovid Shaanxispira, from the upper Miocene deposits of the Linxia Basin, Gansu Province, China, is described here. Shaanxispira is endemic to Northern China and was previously known only from the Lantian area, Shaanxi Province, by two species, S. chowi and S. baheensis. The new ..."
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A new species of the bovid Shaanxispira, from the upper Miocene deposits of the Linxia Basin, Gansu Province, China, is described here. Shaanxispira is endemic to Northern China and was previously known only from the Lantian area, Shaanxi Province, by two species, S. chowi and S. baheensis. The new species, S. linxiaensis nov. sp., is of early Bahean in age, slightly older than the species from the Lantian area. The horncores of the new species are more derived, with large wingshaped anteromedial keels, suggesting the occurrence of a different lineage of Shaanxispira in the Linxia Basin. Although Shaanxispira has homonymously twisted horncores, it is not closely related to other late Miocene bovids with homonymously twisted horncores, like Oioceros and Samotragus. Its phylogenetic status is still in debate, but might be more closely related to the late Miocene “ovibovines.”
Random Numbers and their Effect on Particle Swarm Optimization
"... Particle Swarm Optimization is a relatively new evolutionary computation technique. It is based on the social behaviour of birds flocking or fish schooling. It was designed to find optimal regions in a search space, with the biological idea of swarms in mind. In the Particle Swarm Optimization algor ..."
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Particle Swarm Optimization is a relatively new evolutionary computation technique. It is based on the social behaviour of birds flocking or fish schooling. It was designed to find optimal regions in a search space, with the biological idea of swarms in mind. In the Particle Swarm Optimization algorithm, particles which ‘fly’ around the search space have velocities associated with them. Updating these particles velocities can be described in one succinct equation which essentially updates the velocities of the particles in the system. A large part of this formula is affected by random numbers, as is it is clear from the formula that depending on the size, the random number generated can have a big say in the change of the velocity. The aim of this paper is to explore the effects of using different random number generation techniques, and specifically if it has an impact on the performance of Particle Swarm Optimization on three common optimization problems. 1.
Research Article A Particle Swarm Optimization Variant with an Inner Variable Learning Strategy
"... which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Although Particle SwarmOptimization (PSO) has demonstrated competitive performance in solving global optimization problems, it exhibits some limitations when dealing with optim ..."
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which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Although Particle SwarmOptimization (PSO) has demonstrated competitive performance in solving global optimization problems, it exhibits some limitations when dealing with optimization problems with high dimensionality and complex landscape. In this paper, we integrate some problemoriented knowledge into the design of a certain PSO variant. The resulting novel PSO algorithm with an inner variable learning strategy (PSOIVL) is particularly efficient for optimizing functions with symmetric variables. Symmetric variables of the optimized function have to satisfy a certain quantitative relation. Based on this knowledge, the inner variable learning (IVL) strategy helps the particle to inspect the relation among its inner variables, determine the exemplar variable for all other variables, and then make each variable learn from the exemplar variable in terms of their quantitative relations. In addition, we design a new trap detection and jumping out strategy to help particles escape from local optima. The trap detection operation is employed at the level of individual particles whereas the trap jumping out strategy is adaptive in its nature. Experimental simulations completed for some representative optimization functions demonstrate the excellent performance of PSOIVL. The
Fitness Sharing Particle Swarm Optimization Approach to FACTS Installation for Transmission System Loadability Enhancement
"... transmission networks can enable power systems to accommodate more power transfer with less network expansion cost. The problem to maximize transmission system loadability by determining optimal locations and settings for installations of two types of FACTS devices, namely static var compensator (SV ..."
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transmission networks can enable power systems to accommodate more power transfer with less network expansion cost. The problem to maximize transmission system loadability by determining optimal locations and settings for installations of two types of FACTS devices, namely static var compensator (SVC) and thyristor controlled series compensator (TCSC), is formulated as a mixed discretecontinuous nonlinear optimization problem (MDCP). For solving the MDCP, in the paper, the proposed method with fitness sharing technique involved in the updating process of the particle swarm optimization (PSO) algorithm, can diversify the particles over the search regions as much as possible, making it possible to achieve the optimal solution with a big probability. The modified IEEE14 bus network and a practical power system are used to validate the proposed method.