Particle swarm optimization: Hybridization perspectives and experimental illustrations

Thangaraj, Radha; Pant, Millie; Abraham, Ajith; Bouvry, Pascal

doi:10.1016/j.amc.2010.12.053

Cited by 210 publications

(123 citation statements)

References 54 publications

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“…The hybrid algorithm applied in this work consisted of initiating the minimization with the random search algorithm PSO, taking advantage of its global search character so as to explore the search space, and subsequently apply the deterministic algorithm IP, which is expected to further refine the solution and improve convergence speed. Even though such a combination of algorithms have been already proposed [160][161][162], the quantitative assessment in data reconciliation applications is almost absent. The hybrid procedure combined with the sequential approach was able to provide robust and feasible solutions for all samples analyzed, where both final objective function values and reconciled values were within the confidence region.…”

Section: Discussionmentioning

confidence: 99%

Numerical Aspects of Data Reconciliation in Industrial Applications

et al. 2017

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Abstract:Data reconciliation is a model-based technique that reduces measurement errors by making use of redundancies in process data. It is largely applied in modern process industries, being commercially available in software tools. Based on industrial applications reported in the literature, we have identified and tested different configuration settings providing a numerical assessment on the performance of several important aspects involved in the solution of nonlinear steady-state data reconciliation that are generally overlooked. The discussed items are comprised of problem formulation, regarding the presence of estimated parameters in the objective function; solution approach when applying nonlinear programming solvers; methods for estimating objective function gradients; initial guess; and optimization algorithm. The study is based on simulations of a rigorous and validated model of a real offshore oil production system. The assessment includes evaluations of solution robustness, constraint violation at convergence, and computational cost. In addition, we propose the use of a global test to detect inconsistencies in the formulation and in the solution of the problem. Results show that different settings have a great impact on the performance of reconciliation procedures, often leading to local solutions. The question of how to satisfactorily solve the data reconciliation problem is discussed so as to obtain improved estimates.

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Section: Discussionmentioning

confidence: 99%

Numerical Aspects of Data Reconciliation in Industrial Applications

et al. 2017

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“…El método se repite iterativamente hasta que el valor óptimo se alcanza. La inclusión de la fase del PSO crea una perturbación en la población que, a su vez, ayuda a mantener la diversidad de la población y la producción de una solución óptima [21].…”

Section: Metaheurística De-psounclassified

Estimación de los parámetros del modelo no lineal de promedios móviles usando la metaheuristica de-PSO

Cogollo

Velásquez

Jaramillo

2013

rev.ing.univ.Medellin

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RESUMENLa extensión teórica de los modelos lineales de promedios móviles al caso no lineal es directa y sencilla. Sin embargo, el uso práctico de los modelos no lineales de promedios móviles es limitado debido a la complejidad del espacio de parámetros y la imposibilidad de las establecer derivadas analíticas de la función de estimación. En este artículo, se evalúa el uso del algoritmo híbrido Evolución DiferencialOptimización del Enjambre de Partículas para calcular los parámetros óptimos del modelo no lineal de promedios móviles. Los resultados obtenidos muestran que la técnica meta-heurística utilizada es capaz de calcular con mayor precisión los valores de los parámetros del modelo en comparación con los algoritmos tradicionales. Este hallazgo nos alienta a explorar el uso de las meta-heurísticas en la estimación de los parámetros de otros modelos no lineales de promedios móviles.

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“…It may added that besides DE, other Metaheuristics like genetic algorithms (GA) [66,67], particle swarm optimization (PSO) [68] and artificial bee colony (ABC) [69] may be combined with DEA or the effect of other soft computing techniques [70,71] like artificial neural networks etc. may be tested on DEA.…”

Section: Summary and Directions Of Future Researchmentioning

confidence: 99%

Sustainable educational supply chain performance measurement through DEA and differential evolution: A case on Indian HEI

Jauhar

Pant

Nagar

2017

Journal of Computational Science

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Data envelopment analysis or DEA methodology is employed for assessing the relative efficiency of different homogeneous units. Through DEA one can analyze the areas which need more attention and can suggest measures for improving the performance of different sectors. Through this article, the authors have tried to analyze the relative efficiency of IITR (The Indian Institute of Technology Roorkee), a higher educational institute (HEI) in India. The efficiency of nineteen academic departments of IIT Roorkee is measured with respect to teaching and research. The novlty of the paper is twofold (1) the authiors have considered the environmental aspects (sustainability criteria) while measuring efficiency (2) Differential Evolution (DE) algorithm is employed in accordance with DEA on the fractional model generated for calculating efficiency.

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Particle swarm optimization: Hybridization perspectives and experimental illustrations

Cited by 210 publications

References 54 publications

Numerical Aspects of Data Reconciliation in Industrial Applications

Numerical Aspects of Data Reconciliation in Industrial Applications

Estimación de los parámetros del modelo no lineal de promedios móviles usando la metaheuristica de-PSO

Sustainable educational supply chain performance measurement through DEA and differential evolution: A case on Indian HEI

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