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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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Cited by 80 (0 self)
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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: Basic Concepts, Variants and Applications in Power Systems
, 2008
"... Many areas in power systems require solving one or more nonlinear optimization problems. While analytical methods might suffer from slow convergence and the curse of dimensionality, heuristicsbased swarm intelligence can be an efficient alternative. Particle swarm optimization (PSO), part of the s ..."
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Cited by 77 (12 self)
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Many areas in power systems require solving one or more nonlinear optimization problems. While analytical methods might suffer from slow convergence and the curse of dimensionality, heuristicsbased swarm intelligence can be an efficient alternative. Particle swarm optimization (PSO), part of the swarm intelligence family, is known to effectively solve largescale nonlinear optimization problems. This paper presents a detailed overview of the basic concepts of PSO and its variants. Also, it provides a comprehensive survey on the power system applications that have benefited from the powerful nature of PSO as an optimization technique. For each application, technical details that are required for applying PSO, such as its type, particle formulation (solution representation), and the most efficient fitness functions are also discussed.
Exploring Extended Particle Swarm: A Genetic Programming Approach
 Proceedings of the 2005 Conference on Genetic and Evolutionary Computation
, 2005
"... Particle Swarm Optimisation (PSO) uses a population of particles fly over the fitness landscape in search of an optimal solution. The particles are controlled by forces that encourage each particle to fly back both towards the best point sampled by it and towards the swarm’s best point, while its mo ..."
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Cited by 26 (4 self)
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Particle Swarm Optimisation (PSO) uses a population of particles fly over the fitness landscape in search of an optimal solution. The particles are controlled by forces that encourage each particle to fly back both towards the best point sampled by it and towards the swarm’s best point, while its momentum tries to keep it moving in its current direction. Previous research [17] started exploring the possibility of evolving the force generating equations which control the particles through the use of genetic programming (GP). We independently verify the findings of [17] and then extend it by considering additional meaningful ingredients for the PSO forcegenerating equations, such as global measures of dispersion and position of the swarm. We show that, on a range of problems, GP can automatically generate new PSO algorithms that outperform standard humangenerated as well as some previously evolved ones. Categories and Subject Descriptors
Extended Immune Programming and Oppositebased PSO for Evolving Flexible Beta Basis Function Neural Tree
"... Abstract — In this paper, a new hybrid learning algorithm based on the global optimization techniques, is introduced to evolve the Flexible Beta Basis Function Neural Tree (FBBFNT). The structure is developed using the Extended Immune Programming (EIP) and the Beta parameters and connected weights a ..."
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Cited by 3 (1 self)
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Abstract — In this paper, a new hybrid learning algorithm based on the global optimization techniques, is introduced to evolve the Flexible Beta Basis Function Neural Tree (FBBFNT). The structure is developed using the Extended Immune Programming (EIP) and the Beta parameters and connected weights are optimized using the Oppositebased Particle Swarm Optimization (OPSO) algorithm. The performance of the proposed method is evaluated for time series prediction area and is compared with those of associated methods.
HYBRID OF PARTICLE SWARM OPTIMIZATION WITH EVOLUTIONARY OPERATORS TO FRAGILE IMAGE WATERMARKING BASED DCT
"... Particle swarm optimization (PSO) is a new promising evolutionary algorithm for the optimization and search problem. One problem of PSO is its tendency to trap into local optima due to its mechanism in information sharing. This paper proposes a novel hybrid PSO, namely (HPSO) technique by merging bo ..."
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Particle swarm optimization (PSO) is a new promising evolutionary algorithm for the optimization and search problem. One problem of PSO is its tendency to trap into local optima due to its mechanism in information sharing. This paper proposes a novel hybrid PSO, namely (HPSO) technique by merging both a mutation operator and natural selection to solve the problem of premature convergence. By introducing Cauchy mutation and evolutionary selection strategy based on roulette wheel selection, HPSO could greatly reduce the probability of trapping into local optimum. HPSO is proposed to improve the performance of fragile watermarking based DCT which results in enhancing both the quality of the watermarked image and the extracted watermark. After embedding watermark to the original image in the frequency domain, the conversion of real numbers of the modified coefficients in frequency domain to integer numbers in spatial domain produces some rounding errors problem. This problem results in completely different of the extracted watermark from the embedded watermark. The new developed PSO with evolutionary operators is carried out for correcting the rounding errors by training a translation map used to modify the inverse DCT (IDCT) coefficients from real to integer numbers. The experimental results show the superiority of the proposed algorithm comparing with the standard PSO for improving the performance of DCT fragile watermarking. Besides, it has been shown that the developed PSO is faster in convergence and the obtained results proved to have higher fitness than the other algorithm.
Particle Swarm Optimization: Basic Concepts, Variants and Applications in Power Systems Yamille del Valle, Student Member, IEEE, Ganesh Kumar Venayagamoorthy, Senior Member, IEEE,
"... Abstract—Many areas in power systems require solving one or more nonlinear optimization problems. While analytical methods might suffer from slow convergence and the curse of dimensionality, heuristicsbased swarm intelligence can be an efficient alternative. Particle swarm optimization (PSO), part ..."
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Abstract—Many areas in power systems require solving one or more nonlinear optimization problems. While analytical methods might suffer from slow convergence and the curse of dimensionality, heuristicsbased swarm intelligence can be an efficient alternative. Particle swarm optimization (PSO), part of the swarm intelligence family, is known to effectively solve largescale nonlinear optimization problems. This paper presents a detailed overview of the basic concepts of PSO and its variants. Also, it provides a comprehensive survey on the power system applications that have benefited from the powerful nature of PSO as an optimization technique. For each application, technical details that are required for applying PSO, such as its type, particle formulation (solution representation), and the most efficient fitness functions are also discussed. Index Terms—Classical optimization, particle swarm optimization (PSO), power systems applications, swarm intelligence. I.
Exploring Extended Particle Swarms: A Genetic Programming Approach
 In Proceedings of the 2005 conference on Genetic and evolutionary computation
, 2005
"... Sw arm Optimisation (PSO) uses a population of particles that fly over the fitness landscape in search of an optimal solution. The particles are controlled by forces that encourage each particle to fly back both tow ards the best point sampled by it and tow ards the sw arm's best point, w ile i ..."
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Sw arm Optimisation (PSO) uses a population of particles that fly over the fitness landscape in search of an optimal solution. The particles are controlled by forces that encourage each particle to fly back both tow ards the best point sampled by it and tow ards the sw arm's best point, w ile its momentum tries to keep it moving in its current direction. Previous research started exploring the possibility of evolving the force generating equationsw hich control the particles through the use of genetic programming (GP). We independently verify the findings of the previous research and then extend it by considering additional meaningful ingredients for the PSO forcegenerating equations, such as global measures of dispersion and position of the sw arm. We show that, on a range of problems, GP can automatically generate new PSO algorithms that outperform standard humangenerated asw ell as some previously evolved 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.”
ORIGINAL PAPER Introducing the fractionalorder Darwinian PSO
"... Abstract One of the most wellknown bioinspired algorithms used in optimization problems is the particle swarm optimization (PSO), which basically consists on a machinelearning technique loosely inspired by birds flocking in search of food. More specifically, it consists of a number of particles th ..."
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Abstract One of the most wellknown bioinspired algorithms used in optimization problems is the particle swarm optimization (PSO), which basically consists on a machinelearning technique loosely inspired by birds flocking in search of food. More specifically, it consists of a number of particles that collectively move on the search space in search of the global optimum. The Darwinian particle swarm optimization (DPSO) is an evolutionary algorithm that extends the PSO using natural selection, or survival of the fittest, to enhance the ability to escape from local optima. This paper firstly presents a survey on PSO algorithms mainly focusing on the DPSO. Afterward, a method for controlling the convergence rate of the DPSO using fractional calculus (FC) concepts is proposed. The fractionalorder optimization algorithm, denoted as FODPSO, is tested using several wellknown functions, and the relationship between the