A two-swarm cooperative particle swarms optimization

Sun, Shiyuan; Li, Jianwei

doi:10.1016/j.swevo.2013.10.003

Cited by 67 publications

(32 citation statements)

References 21 publications

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“…Secondly, update X k i by (27), then test each particle to judge whether it satisfies the constraints of AFS and IS methods. If no, recreate a particle until it satisfies the condition.…”

Section: And Initialize a Random Velocitymentioning

confidence: 99%

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Online‐Offline Optimized Motion Profile for High‐Dynamic Positioning of Ultraprecision Dual Stage

2018

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The wafer stage in dual-stage lithographic system is an air-bearing servo motion platform requiring high positioning accuracy and high transient performance. However, the residual vibration, resulting from almost zero damping, high velocity, parallel decoupling structure, and direct drive, brings about unacceptable overshoot and settling time. To suppress these unfavorable elements in high dynamic motion, a novel motion profile planning method combined with input shaping is proposed in this paper. Firstly, a trajectory named all free S-curve (AFS-curve) is derived, which has less constraints and better performance than traditional S-curve profile. Then, AFS-curve combined with a zero vibration shaper (ZV) is developed to further suppress residual vibration. Due to the very complex parameter adjustment, the online tuning may cause system oscillation that leads to damage of the precision stage. This paper, furthermore, proposes an online-offline method to optimize the parameters in the motion profile. Online step is performed to collect input and output data. Offline step includes the system model identification based on I/O data and parameter self-learning based on particle swarm optimization (PSO). The simulation and experimental results indicate that the proposed method achieves significant reduction of the positioning time and the overshoot in the dual-stage system.

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“…Secondly, update X k i by (27), then test each particle to judge whether it satisfies the constraints of AFS and IS methods. If no, recreate a particle until it satisfies the condition.…”

Section: And Initialize a Random Velocitymentioning

confidence: 99%

“…Thus, a reliable optimization algorithm [20][21][22][23] is more suitable for the trajectory planning. Furthermore, both of the overshoot and error should be considered in the optimization object, so a multiobjective optimization based on PSO [24][25][26][27][28][29] (particle swarm optimization) is adopted in this paper.…”

Section: Introductionmentioning

confidence: 99%

Online‐Offline Optimized Motion Profile for High‐Dynamic Positioning of Ultraprecision Dual Stage

2018

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“…A new method referred to as SHPSO was proposed by Yadav and Deep [51,52] based on the idea of the shrinking hypersphere versus particle generation. Additional noteworthy optimization algorithms, such as two-swarm COPSO [48], DSS-MDE [54], COMDE [39], DECV [36], time adaptive hybrid PSO [4], and A-DDE [37] were also presented in the literature.…”

Section: Particle Swarm Optimizationmentioning

confidence: 99%

Gravitational swarm optimizer for global optimization

Yadav

Deep

Kim

et al. 2016

Swarm and Evolutionary Computation

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In this article, a new meta-heuristic method is proposed by combining particle swarm optimization (PSO) and gravitational search in a coherent way. The advantage of swarm intelligence and the idea of a force of attraction between two particles are employed collectively to propose an improved meta-heuristic method for constrained optimization problems. Excellent constraint handling is always required for the success of any constrained optimizer. In view of this, an improved constraint-handling method is proposed which was designed in alignment with the constitutional mechanism of the proposed algorithm. The design of the algorithm is analyzed in many ways and the theoretical convergence of the algorithm is also established in the article.The efficiency of the proposed technique was assessed by solving a set of 24 constrained problems and 15 unconstrained problems which have been proposed in IEEE-CEC sessions 2006 and 2015, respectively. The results are compared with 11 state-of-the-art algorithms for constrained problems and 6 state-of-the-art algorithms for unconstrained problems. A variety of ways are considered to examine the ability of the proposed algorithm in terms of its converging ability, success, and statistical behavior. The performance of the proposed constraint-handling method is judged by analyzing its ability to produce a feasible population. It was concluded that the proposed algorithm performs efficiently with good results as a constrained optimizer.

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“…A combination of random coefficient has been used in previous studies [16]- [17]. The reports of these articles indicate better efficiency by combining random coefficients.…”

Section: Using the Random Coefficients In Combinationmentioning

confidence: 99%

Adaptive Adjustment of PSO Coefficients Taking the Notion from the Bee Behavior in Collecting Nectar

Fadavi¹,

Faez²,

Famili³

2016

IJECE

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In particle swarm optimization, a set of particles move towards the global optimum point according to their experience and experience of other particles. Parameters such as particle rate, particle best experience, the best experience of all the particles and particle current position are used to determine the next position of each particle. Certain relationships received the input parameters and determined the next position of each particle. In this article, the relationships are accurately assessed and the amount of the effect of input parameters is horizontally set. To set coefficients adaptively, the notion is taken from bee behavior in collecting nectar. This method was implemented on software and examined in the standard search environments. The obtained results indicate the efficiency of this method in increasing the rate of convergence of particles towards the global optimum. Keyword:Adaptive is composed of a set of particles. The aim of all the particles is approaching the optimum response and reducing error. The error of each particle is particle distance to response. Each particle can be a potential response. Each particle determines its future position by consulting with other particles and its experiences. The position of each particle is a result of its experiences and other particles' experiences. For example, we consider a person as a smart particle and the purpose as buying a suitable automobile. The person pays attention to two factors in buying a suitable automobile; First, his last experiences of buying an automobile and second, consulting with other people and asking their opinion about experiences of buying an automobile. The person, regarding his experiences and others' experiences in buying an automobile, selects his optimum automobile. Figure 1 indicates how a hypothetical particle performs in the optimization algorithm of particle swarm optimization. The horizontal axis indicates the scope of search space and the vertical axis indicates the amount of error according to consistent function. As shown in Figure 1, there is a search space in which a particle tries to reach a global optimum. x(t) is the position of a particle at the time t, v(t) is the rate of a particle at the time t, p best (t) is the best experience of a particle to the time t and g best (t) is the best experience of all the particles to the time t. In PSO method, each particle tends to move towards its best experience and best experience of other particles. p best -x(t) is the distance of particle to its best experience and g best -x(t) is the distance of particle to the best experience of other particles. The rate v (t+1) is the resultant of the two

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A two-swarm cooperative particle swarms optimization

Cited by 67 publications

References 21 publications

Online‐Offline Optimized Motion Profile for High‐Dynamic Positioning of Ultraprecision Dual Stage

Online‐Offline Optimized Motion Profile for High‐Dynamic Positioning of Ultraprecision Dual Stage

Gravitational swarm optimizer for global optimization

Adaptive Adjustment of PSO Coefficients Taking the Notion from the Bee Behavior in Collecting Nectar

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