Simulation of a new hybrid particle swarm optimization algorithm

Noel, Mathew Mithra; Jannett, T.C.

doi:10.1109/ssst.2004.1295638

Cited by 41 publications

(34 citation statements)

References 5 publications

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“…The hybrid PSO was first proposed in 1998 [43], in which a standard selection mechanism is integrated with the PSO. A new hybrid gradient descent PSO (HGPSO), which is integrated with gradient information to achieve faster convergence without getting trapped in the local minima, is proposed by Noel and Jannett [16]. However, the computational demand of the HGPSO is increased by the process of the gradient descent.…”

mentioning

confidence: 99%

“…The PSO's mutating space is dynamically varying along the search based on the properties of the wavelet function. The resulting mutation operation aids the hybrid PSO to perform more efficiently and provides faster convergence than the PSO with constriction and inertia weight factors [9] and other hybrid PSOs [1], [16], [17], [28] in solving a suite of benchmark test functions. In addition, it achieves better and more stable solution quality.…”

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confidence: 99%

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Hybrid Particle Swarm Optimization With Wavelet Mutation and Its Industrial Applications

Ling

Chan

et al. 2008

IEEE Trans. Syst., Man, Cybern. B

233

128

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Abstract-A new hybrid particle swarm optimization (PSO) that incorporates a wavelet-theory-based mutation operation is proposed. It applies the wavelet theory to enhance the PSO in exploring the solution space more effectively for a better solution. A suite of benchmark test functions and three industrial applications (solving the load flow problems, modeling the development of fluid dispensing for electronic packaging, and designing a neuralnetwork-based controller) are employed to evaluate the performance and the applicability of the proposed method. Experimental results empirically show that the proposed method significantly outperforms the existing methods in terms of convergence speed, solution quality, and solution stability.

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mentioning

confidence: 99%

mentioning

confidence: 99%

Hybrid Particle Swarm Optimization With Wavelet Mutation and Its Industrial Applications

Ling

Chan

et al. 2008

IEEE Trans. Syst., Man, Cybern. B

233

128

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show abstract

“…The simples approach to calculate the gradient is HGPSO (Noel & Jannett 2004) is the numerical approximation of the gradient.…”

Section: B Improved Pso Algorithmmentioning

confidence: 99%

Improvement of PSO Algorithm by Memory-Based Gradient Search—Application in Inventory Management

Varga¹,

Király²,

Abonyi³

2013

Swarm Intelligence and Bio-Inspired Computation

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-Advanced inventory management in complex supply chains requires effective and robust nonlinear optimization due to the stochastic nature of supply and demand variations. Application of estimated gradients can boost up the convergence of Particle Swarm Optimization (PSO) algorithm but classical gradient calculation cannot be applied to stochastic and uncertain systems. In these situations Monte-Carlo (MC) simulation can be applied to determine the gradient. We developed a memory based algorithm where instead of generating and evaluating new simulated samples the stored and shared former function evaluations of the particles are sampled to estimate the gradients by local weighted least squares regression. The performance of the resulted regional gradient-based PSO is verified by several benchmark problems and in a complex application example where optimal reorder points of a supply chain are determined.

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“…As with genetic algorithms some effort has gone into improving the performance of particle swarms in this area, with the introduction of both hybrid particle swarms which employ a local search, Shu-Kai et al (2004), or those which make specific use of gradient information in the velocity update equation, Noewl and Jannett (2004) and Ninomiya and Zhang (2008).…”

Section: The Basic Particle Swarmmentioning

confidence: 99%

“…Rather than use a local search strategy, Noewl and Jannett (2004) modified the particle swarm update equation to make use of any available gradient information by adopting a gradient descent term instead of a nostalgia term. The components of the updated velocity were therefore due to the inertia of each particle, the location of the global best point and a step in the steepest descent direction.…”

Section: Existing Hybridized Particle Swarmsmentioning

confidence: 99%

The development of a hybridized particle swarm for kriging hyperparameter tuning

Toal

Bressloff

Keane

et al. 2011

Engineering Optimization

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Optimizations involving high fidelity simulations can become prohibitively expensive when an exhaustive search is employed. To remove this expense a surrogate model is often constructed. One of the most popular techniques for the construction of such a surrogate model is that of kriging. However, the construction of a kriging model requires the optimization of a multi-model likelihood function, the cost of which, can approach that of the high fidelity simulations upon which the model is based. The following paper describes the development of a hybridized particle swarm algorithm which aims to reduce the cost of this likelihood optimization by drawing on an efficient adjoint of the likelihood. This hybridized tuning strategy is compared to a number of other strategies with respect to the inverse design of an airfoil as well as the optimization of an airfoil for minimum drag at a fixed lift.

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Simulation of a new hybrid particle swarm optimization algorithm

Cited by 41 publications

References 5 publications

Hybrid Particle Swarm Optimization With Wavelet Mutation and Its Industrial Applications

Hybrid Particle Swarm Optimization With Wavelet Mutation and Its Industrial Applications

Improvement of PSO Algorithm by Memory-Based Gradient Search—Application in Inventory Management

The development of a hybridized particle swarm for kriging hyperparameter tuning

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