A Hybrid Algorithm Based on PSO and Simulated Annealing and Its Applications for Partner Selection in Virtual Enterprise

Zhao, Fuqing; Zhang, Qiuyu; Yu, Dongmei; Chen, Xuhui; Yang, Yue

doi:10.1007/11538059_40

Cited by 15 publications

(13 citation statements)

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“…This indicates that PSO may be of value for improving or refining the quality of an initial highquality population. Others (Li et al, 2006;Zhao et al, 2005) have also shown, in problems related to other fields, that combining PSO with a local search process may be a fruitful endeavor. Another part of the below average performance we observed can be attributed to the forest planning problem itself.…”

Section: Discussionmentioning

confidence: 99%

Pitfalls and potential of particle swarm optimization for contemporary spatial forest planning

Shan¹,

Bettinger²,

Cieszewski³

et al. 2012

For. syst.

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We describe here an example of applying particle swarm optimization (PSO) -a population-based heuristic technique -to maximize the net present value of a contemporary southern United States forest plan that includes spatial constraints (green-up and adjacency) and wood flow constraints. When initiated with randomly defined feasible initial conditions, and tuned with some appropriate modifications, the PSO algorithm gradually converged upon its final solution and provided reasonable objective function values. However, only 86% of the global optimal value could be achieved using the modified PSO heuristic. The results of this study suggest that under random-start initial population conditions the PSO heuristic may have rather limited application to forest planning problems with economic objectives, wood-flow constraints, and spatial considerations. Pitfalls include the need to modify the structure of PSO to both address spatial constraints and to repair particles, and the need to modify some of the basic assumptions of PSO to better address contemporary forest planning problems. Our results, and hence our contributions, are contrary to earlier work that illustrated the impressive potential of PSO when applied to stand-level forest planning problems or when applied to a high quality initial population.Key words: mathematical programming; heuristic; modeling technique; forest management. ResumenDificultades y posibilidades del algoritmo de optimización de enjambre de partículas para la planificación contemporánea espacial del bosque Se describe aquí un ejemplo de la aplicación de la optimización de enjambre de partículas (PSO) -una técnica heurís-tica basada en la población -para maximizar el valor presente neto de un moderno plan de gestión del bosque del sur de los Estados Unidos, que incluye limitaciones espaciales y restricciones del flujo de madera. Cuando se inicia con condiciones iniciales factibles definidas aleatoriamente, y en sintonía con algunas modificaciones adecuadas, el algoritmo PSO converge gradualmente sobre su solución final y suministra los valores de la función objetivo. Sin embargo, sólo el 86% del valor global óptimo podría lograrse usando la heurística PSO modificada. Los resultados de este estudio sugieren que bajo condiciones de arranque aleatorio de la población inicial, la heurística PSO puede tener una aplicación más bien limitada a los problemas de planificación forestal con objetivos económicos, restricciones de flujo de madera y consideraciones espaciales. Las dificultadas incluyen la necesidad de modificar la estructura de PSO para abordar tanto las limitaciones espaciales como para reparar las partículas, y la necesidad de modificar algunos de los supuestos básicos de PSO para abordar mejor los problemas contemporáneos de la planificación forestal. Nuestros resultados, y por lo tanto nuestra aportación, son contrarios a trabajos anteriores que ilustran el impresionante potencial de PSO cuando se aplica a problemas de planificación forestal a nivel de rodal o cuando se aplic...

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Section: Discussionmentioning

confidence: 99%

Pitfalls and potential of particle swarm optimization for contemporary spatial forest planning

Shan¹,

Bettinger²,

Cieszewski³

et al. 2012

For. syst.

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“…Zhao et al (2005) proposed a hybrid algorithm based on PSO and simulated annealing and its applications for partner selection in virtual enterprise and supply chain management. Xu and Yan (2011) focused on the vendor selection problem (VSP) for material supply in large-scale water conservancy and hydropower construction projects, they used PSO to solve this problem in a fuzzy environment.…”

Section: Introductionmentioning

confidence: 99%

Application of particle swarm optimisation with backward calculation to solve a fuzzy multi-objective supply chain master planning model

Grillo

Peidro

Alemany

2015

IJBIC

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InderscienceGrillo Espinoza, H.; Peidro Payá, D.; Alemany Díaz, MDM.; Mula, J. (2015). Application of particle swarm optimisation with backward calculation to solve a fuzzy multi-objective supply chain master planning model. International Journal of Bio-Inspired Computation. 7(3): 157-169. doi:10.1504/IJBIC.2015 Copyright © 200x Inderscience Enterprises Ltd. APPLICATION OF PARTICLE SWARM OPTIMIZATION WITH BACKWARD CALCULATION (PSO-B) TO SOLVE A FUZZY MULTIOBJECTIVE SUPPLY CHAIN MASTER PLANNING MODEL Abstract:Traditionally, supply chain planning problems consider variables with uncertainty associated with uncontrolled factors. These factors have been normally modelled by complex methodologies where the seeking solution process often presents high scale of difficulty. This work presents the fuzzy set theory as a tool to model uncertainty in supply chain planning problems and proposes the particle swarm optimization (PSO) metaheuristics technique combined with a backward calculation as a solution method. The aim of this combination is to present a simple effective method to model uncertainty, while good quality solutions are obtained with metaheuristics due to its capacity to find them with satisfactory computational performance in complex problems, in a relatively short time period.

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“…Earlier reported works demonstrated that by combining SA with PSO, significant improvements are achieved for different engineering problems, like partner selection in virtual enterprise [7], job shop scheduling problem [8], holon task allocation scheme [9], energy consumption reduction in embedded systems [10], and numerical verification of several benchmark functions [11]. Zhao et al [7,9] and Jamili et al [8] combined SA with PSO for solving different practical problems, where SA starts with the global best solution generated by PSO. These hybrid algorithms [7][8][9] can be easily converted to basic PSO by ignoring the SA subroutine, and, at the same time, it can be used as a conventional SA by assuming population size to be one particle.…”

Section: Introductionmentioning

confidence: 99%

“…Zhao et al [7,9] and Jamili et al [8] combined SA with PSO for solving different practical problems, where SA starts with the global best solution generated by PSO. These hybrid algorithms [7][8][9] can be easily converted to basic PSO by ignoring the SA subroutine, and, at the same time, it can be used as a conventional SA by assuming population size to be one particle. It is demonstrated in [7][8][9] that the hybrid method outperforms conventional GA, SA, and PSO not only by providing quality solution but also by exhibiting greater convergence speed.…”

Section: Introductionmentioning

confidence: 99%

Two-Dimensional IIR Filter Design Using Simulated Annealing Based Particle Swarm Optimization

Dhabal¹,

Venkateswaran

2014

Journal of Optimization

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We present a novel hybrid algorithm based on particle swarm optimization (PSO) and simulated annealing (SA) for the design of two-dimensional recursive digital filters. The proposed method, known as SA-PSO, integrates the global search ability of PSO with the local search ability of SA and offsets the weakness of each other. The acceptance criterion of Metropolis is included in the basic algorithm of PSO to increase the swarm’s diversity by accepting sometimes weaker solutions also. The experimental results reveal that the performance of the optimal filter designed by the proposed SA-PSO method is improved. Further, the convergence behavior as well as optimization accuracy of proposed method has been improved significantly and computational time is also reduced. In addition, the proposed SA-PSO method also produces the best optimal solution with lower mean and variance which indicates that the algorithm can be used more efficiently in realizing two-dimensional digital filters.

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A Hybrid Algorithm Based on PSO and Simulated Annealing and Its Applications for Partner Selection in Virtual Enterprise

Cited by 15 publications

References 13 publications

Pitfalls and potential of particle swarm optimization for contemporary spatial forest planning

Pitfalls and potential of particle swarm optimization for contemporary spatial forest planning

Application of particle swarm optimisation with backward calculation to solve a fuzzy multi-objective supply chain master planning model

Two-Dimensional IIR Filter Design Using Simulated Annealing Based Particle Swarm Optimization

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