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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.
Cooperatively Coevolving Particle Swarms for Large Scale Optimization
 IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION
, 2011
"... This paper presents a new cooperative coevolving particle swarm optimization (CCPSO) algorithm in an attempt to address the issue of scaling up particle swarm optimization (PSO) algorithms in solving largescale optimization problems (up to 2000 realvalued variables). The proposed CCPSO2 builds on ..."
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Cited by 24 (7 self)
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This paper presents a new cooperative coevolving particle swarm optimization (CCPSO) algorithm in an attempt to address the issue of scaling up particle swarm optimization (PSO) algorithms in solving largescale optimization problems (up to 2000 realvalued variables). The proposed CCPSO2 builds on the success of an early CCPSO that employs an effective variable grouping technique random grouping. CCPSO2 adopts a new PSO position update rule that relies on Cauchy and Gaussian distributions to sample new points in the search space, and a scheme to dynamically determine the coevolving subcomponent sizes of the variables. On highdimensional problems (ranging from 100 to 2000 variables), the performance of CCPSO2 compared favorably against a stateoftheart evolutionary algorithm sepCMAES, two existing PSO algorithms, and a cooperative coevolving differential evolution algorithm. In particular, CCPSO2 performed significantly better than sepCMAES and two existing PSO algorithms on more complex multimodal problems (which more closely resemble realworld problems), though not as well as the existing algorithms on unimodal functions. Our experimental results and analysis suggest that CCPSO2 is a highly competitive optimization algorithm for solving largescale and complex multimodal optimization problems.
Origin of bursts
 In GECCO ’07: Proceedings of the 2007 GECCO Conference Companion on Genetic and Evolutionary Computation
, 2007
"... The phenomenon of particle bursts, a wellknown feature of PSO is investigated. Their origin is concluded to lie in multiplicative stochasticity, previously encountered in the study of first order stochastic difference equations. The work here demonstrates that bursts contribute to fattening of the ..."
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The phenomenon of particle bursts, a wellknown feature of PSO is investigated. Their origin is concluded to lie in multiplicative stochasticity, previously encountered in the study of first order stochastic difference equations. The work here demonstrates that bursts contribute to fattening of the tail of the particle position distribution and that these tails are well described by power laws. It is argued that recombinant PSO, a competitive PSO variant without multiplicative randomness, is burstfree. Categories and Subject Descriptors
Particle Swarm Optimization Containing Plural Swarms
"... In this study, we proposes a modified particle swarm optimization (PSO) called plural PSO (PPSO) for bustout the local optimum solution. The feature of PPSO is that the swarm of PPSO is not one but plural. The plural swarms share an information of the best position in each swarm. Except the swarm ..."
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In this study, we proposes a modified particle swarm optimization (PSO) called plural PSO (PPSO) for bustout the local optimum solution. The feature of PPSO is that the swarm of PPSO is not one but plural. The plural swarms share an information of the best position in each swarm. Except the swarm including the best particle in whole swarms, all the particles are repositioned to escape the local optima. We investigate behaviors of PPSO and confirm its efficiency in multimodal functions. 1.
Bare Bones Particle Swarms with Jumps
"... Abstract. Bare Bones PSO was proposed by Kennedy as a model of PSO dynamics. Dependence on velocity is replaced by sampling from a Gaussian distribution. Although Kennedy’s original formulation is not competitive to standard PSO, the addition of a componentwise jumping mechanism, and a tuning of th ..."
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Abstract. Bare Bones PSO was proposed by Kennedy as a model of PSO dynamics. Dependence on velocity is replaced by sampling from a Gaussian distribution. Although Kennedy’s original formulation is not competitive to standard PSO, the addition of a componentwise jumping mechanism, and a tuning of the standard deviation, can produce a comparable optimisation algorithm. This algorithm, Bare Bones with Jumps, exists in a variety of formulations. Two particular models are empirically examined in this paper and comparisons are made to canonical PSO and standard Bare Bones.
This article has been accepted for inclusion in a future issue of this journal. Content is final as presented, with the exception of pagination. IEEE TRANSACTIONS ON VERY LARGE SCALE INTEGRATION (VLSI) SYSTEMS 1 Gated Decap: Gate Leakage Control of OnChi
"... Abstract—To minimize the leakage power dissipation of presentday onchip Decaps, we propose a gated decoupling capacitor (GDecap) technique that deactivates a Decap when it is not needed. The application of the proposed GDecap technique on an eightway clockgated clustered pipeline showed that on ..."
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Abstract—To minimize the leakage power dissipation of presentday onchip Decaps, we propose a gated decoupling capacitor (GDecap) technique that deactivates a Decap when it is not needed. The application of the proposed GDecap technique on an eightway clockgated clustered pipeline showed that on average, 41.7 % Decap leakage power was reduced, with negligible ( 0 037%) worstcase performance degradation, at the 70nm technology node. GDecap design incurred an area overhead of around 5.36 % when compared with a conventional Decap design. Index Terms—Capacitance, lowpower design, VLSI. I.
A Modified Algorithm of Bare Bones Particle Swarm Optimization
"... Bare bones particle swarm optimization (PSO) greatly simplifies the particles swarm by stripping away the velocity rule, but performance seems not good as canonical one in some test problems. Some studies try to replace the sampling distribution to improve the performance, but there are some problem ..."
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Bare bones particle swarm optimization (PSO) greatly simplifies the particles swarm by stripping away the velocity rule, but performance seems not good as canonical one in some test problems. Some studies try to replace the sampling distribution to improve the performance, but there are some problems in the algorithm itself. This paper proposes a modified algorithm to solve these problems. In addition to some benchmark test functions, we also conducted an application of realworld time series forecasting with support vector regression to evaluate the performance of the proposed PSO algorithm. The results indicate that the modified bare bones particle swarm optimization can be an efficient alternative due to the smaller confidence intervals and fast convergence characteristics.
Volume 2, Number1, March2008 pp. 23—28 LEVY VELOCITY THRESHOLD PARTICLE SWARM OPTIMIZATION
, 2007
"... Abstract. Velocity threshold plays an important role in the particle swarm optimization. In this paper, a novel stochastic velocity threshold automation strategy is proposed by incorporated with Lévy probability distribution. As is known, Lévy probability distribution has an infinite second moment a ..."
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Abstract. Velocity threshold plays an important role in the particle swarm optimization. In this paper, a novel stochastic velocity threshold automation strategy is proposed by incorporated with Lévy probability distribution. As is known, Lévy probability distribution has an infinite second moment and is likely to generate an offspring that is far away from its parent. Therefore, this method employs a larger capability of the global exploration for each particle. Simulation results has shown the proposed strategy is effective and efficient.
and Search General
"... Simplified forms of the particle swarm algorithm are very beneficial in contributing to understanding of what makes a PSO swarm function in the way that it does. One of these forms, PSO with discrete recombination, is analyzed in depth, demonstrating not just improvements in performance to a standar ..."
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Simplified forms of the particle swarm algorithm are very beneficial in contributing to understanding of what makes a PSO swarm function in the way that it does. One of these forms, PSO with discrete recombination, is analyzed in depth, demonstrating not just improvements in performance to a standard PSO algorithm, but also significantly different behavior with a reduction in bursting patterns due to the removal of stochastic components from the update equations. This altered behavior accompanied by equal and improved performance leads to conjectures that bursts are not generally efficacious in the optimization process.
CEC IEEE Comparing lbest PSO Niching algorithms Using Different Position Update
"... Abstract — Niching is an important technique for multimodal optimization in Evolutionary Computation. Most existing niching algorithms are evaluated using only 1 or 2 dimensional multimodal functions. However, it remains unclear how these niching algorithms perform on higher dimensional multimodal p ..."
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Abstract — Niching is an important technique for multimodal optimization in Evolutionary Computation. Most existing niching algorithms are evaluated using only 1 or 2 dimensional multimodal functions. However, it remains unclear how these niching algorithms perform on higher dimensional multimodal problems. This paper compares several schemes of PSO update rules, and examines the effects of incorporating these schemes into a lbest PSO niching algorithm using a ring topology. Subsequently a new Cauchy and Gaussian distributions based PSO (CGPSO) is proposed. Our experiments suggest that CGPSO seems to be able to locate more global peaks than other PSO variants on multimodal functions which typically have many global peaks but very few local peaks. I.